Bibliografie tematiche / Computational economics

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Letteratura scientifica selezionata sul tema "Computational economics"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 9 giugno 2025

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Articoli di riviste sul tema "Computational economics"

Sadiku, Matthew N. O., Mahamadou Tembely, and Sarhan M. Musa. "Computational Economics." International Journal of Engineering Research 6, no. 11 (2017): 464. http://dx.doi.org/10.5958/2319-6890.2017.00065.4.

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Judd, Kenneth, and Scott E. Page. "Computational Public Economics." Journal of Public Economic Theory 6, no. 2 (May 2004): 195–202. http://dx.doi.org/10.1111/j.1467-9779.2004.00164.x.

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Trisetyarso, Agung. "Quantum Computational Economics." Procedia Computer Science 216 (2023): 3. http://dx.doi.org/10.1016/j.procs.2022.12.103.

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Judd, Kenneth L. "Computational economics and economic theory: Substitutes or complements?" Journal of Economic Dynamics and Control 21, no. 6 (June 1997): 907–42. http://dx.doi.org/10.1016/s0165-1889(97)00010-9.

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McAdam, Peter. "Handbook of computational economics." Journal of Economic Dynamics and Control 22, no. 3 (March 1998): 483–87. http://dx.doi.org/10.1016/s0165-1889(97)00094-8.

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Tesfatsion, Leigh. "Agent-based computational economics." Scholarpedia 2, no. 2 (2007): 1970. http://dx.doi.org/10.4249/scholarpedia.1970.

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ORUN, EMRE. "Complexity Economics And Agent- Based Computational Economics." Akademi Sosyal Bilimler Dergisi 7, no. 19 (January 30, 2020): 48–62. http://dx.doi.org/10.34189/asbd.7.19.004.

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Miranda, Mario J. "Teaching Computational Economics in an Applied Economics Program." Computational Economics 25, no. 3 (June 2005): 229–54. http://dx.doi.org/10.1007/s10614-005-2207-x.

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Schönbucher, Philipp. "Applied Computational Economics and Finance." Journal of the American Statistical Association 99, no. 466 (June 2004): 565–66. http://dx.doi.org/10.1198/jasa.2004.s337.

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Amman, Hans M. "The JEDC and computational economics." Journal of Economic Dynamics and Control 21, no. 6 (June 1997): 905–6. http://dx.doi.org/10.1016/s0165-1889(97)00009-2.

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Tesi sul tema "Computational economics"

Pugh, David. "Essays in computational economics." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/9882.

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The focus of my PhD research has been on the acquisition of computational modeling and simulation methods used in both theoretical and applied Economics. My first chapter provides an interactive review of finite-difference methods for solving systems of ordinary differential equations (ODEs) commonly encountered in economic applications using Python. The methods surveyed in this chapter, as well as the accompanying code and IPython lab notebooks should be of interest to any researcher interested in applying finite-difference methods for solving ODEs to economic problems. My second chapter is an empirical analysis of the evolution of the distribution of bank size in the U.S. This paper assesses the statistical support for Zipf's Law (i.e., a power law, or Pareto, distribution with a scaling exponent of α = 2) as an appropriate model for the upper tail of the distribution of U.S. banks. Using detailed balance sheet data for all FDIC regulated banks for the years 1992 through 2011, I find significant departures from Zipf's Law for most measures of bank size inmost years. Although Zipf's Law can be statistically rejected, a power law distribution with α of roughly 1.9 statistically outperforms other plausible heavy-tailed alternative distributions. In my final chapter, which is based on joint work with Dr. David Comerford, I apply computational methods to model the relationship between per capita income and city size. A well-known result from the urban economics literature is that a monopolistically competitive market structure combined with internal increasing returns to scale can be used to generate log-linear relations between income and population. I extend this theoretical framework to allow for a variable elasticity of substitution between factors of production in a manner similar to Zhelobodko et al. (2012). Using data on Metropolitan Statistical Areas (MSAs) in the U.S. I find evidence that supports what Zhelobodko et al. (2012) refer to as "increasing relative love for variety (RLV)." Increasing RLV generates procompetitive effects as market size increases which means that IRS, whilst important for small to medium sized cities, are exhausted as cities become large. This has important policy implications as it suggests that focusing intervention on creating scale for small populations is potentially much more valuable than further investments to increase market size in the largest population centers.

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Jelonek, Piotr Zbigniew. "Essays on computational economics." Thesis, University of Leicester, 2014. http://hdl.handle.net/2381/28644.

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This text consists of two parts. In chapters 2-3 the methods are developed that enable the application of tempered stable distributions to measuring and simulating macroeconomic uncertainties. In contrast to the tools used in finance, these results are applicable to low frequency aggregated data, which typically displays tails of moderate gravity. Thus thay are particularly useful in modelling macroeconomic densities. The new methods may be readily employed in Monte Carlo simulations of possibly skewed, moderately heavy-tailed random variates with arbitrary excess kurtosis. In chapter 4 a computational model of endogenous network formation for the inter-bank overnight lending market is proposed. The structure of this market emerges from interactions of heterogeneous agents who are endowed with assets, liabilities and take into account investment risk. As all the banks are large and their trading affects the prices of risky assets, the costs of price slippage breaks the symmetry of portfolio problem, making inter-bank borrowing and lending more desirable. The model takes into account three channels of contagion - bankruptcy cascades, common component of risky asset returns and erosion of liquidity. The network formation algorithm outputs the ensemble of optimal transactions, the outcome of the corresponding link formation process is pairwise stable. This framework is next employed to investigate the stability of the endogenously generated banking systems.

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Grinis, Inna. "Essays in applied computational economics." Thesis, London School of Economics and Political Science (University of London), 2017. http://etheses.lse.ac.uk/3580/.

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This thesis presents four distinct essays that lie at the intersection of economics and computation. The first essay constructs an abstract framework for defining skills gaps, mismatches and shortages geometrically and thinking about these phenomena in a unified, formal way. It then develops a job matching model with imperfect information, in which skills mismatches influence the job application decisions of the workers, while skills gaps and shortages shape the competition for workers on the resulting bipartite job applications network. The tools proposed in this chapter could in future work be employed as the main ingredients of an agent-based model used to investigate how skills gaps, mismatches and shortages affect equilibrium outcomes. The second chapter designs and tests machine learning algorithms to classify 33 million UK online vacancy postings into STEM and non-STEM jobs based on the keywords collected from the vacancy descriptions and job titles. The goal is to investigate whether jobs in “non-STEM” occupations (e.g. Graphic Designers, Economists) also require and value STEM knowledge and skills (e.g. “Microsoft C#”, “Systems Engineering”), thereby contributing to the debate on whether or not the “STEM pipeline leakage” – the fact that less than half of STEM graduates in the UK work in STEM occupations - should be considered as highly problematic. Chapter 3 relates to empirical growth. It proposes a programming algorithm, called “iterative Fit and Filter” (iFF), that extracts trend growth as a sequence of medium/long term average growth rates, and applies it on a sample of over 150 countries. The paper then develops an econometric framework that relates the conditional probabilities of up and down-shifts in trend growth next year to the country's current characteristics, e.g. the growth environment, level of development, demographics, institutions, etc. Finally, Chapter 4 studies credit risk spillovers in financial networks by modelling default as a multi-stage disease with each credit-rating corresponding to a new infection phase. The paper derives analytical and proposes computer simulation-based indicators of systemic importance and vulnerability, then applies them in the context of the Eurozone sovereign debt crisis.

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Balikcioglu, Metin. "Essays on Environmental and Computational Economics." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-12032008-210449/.

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The study consists of three separate essays. The first essay reassesses and extends the papers by Pindyck (2000, 2002) which analyze the effects of uncertainty and irreversibility on the timing of emissions reduction policy. It is shown that proposed solutions for some of the optimal stopping problems introduced in these papers are incorrect. Correct solutions are provided for both the incorrect special cases and the general model through a numerical method since closed form solutions do not exist for these problems. In the second essay, singular control framework is employed in order to allow for gradual emission reduction instead of once-for-all type policies. The solution for the model is obtained using the numerical method introduced in the last essay. The effects of uncertainty and irreversibility on optimal emission reduction policy are investigated. The model is illustrated for greenhouse gas mitigation in the context of climate change problem and some of the model parameters are estimated using a state space model. In the third essay, a unified numerical method is introduced for solving multidimensional singular and impulse control models. The link between regime switching and singular/impulse control problems is established. This link results in a convenient representation of optimality conditions for the numerical method. After solving the optimality conditions at a discrete set of points, an approximate solution can be obtained by solving an extended vertical linear complementarity problem using a variety of techniques. The numerical approach is illustrated with four examples from economics and finance literature.

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Schuster, Stephan. "Applications in agent-based computational economics." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.556466.

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A constituent feature of adaptive complex systems are non-linear feedback mechanisms between actors. These mechanisms are often difficult to model and analyse. One pos- sibility of modelling is given by Agent-based Computational Economics (ACE), which uses computer simulation methods to represent such systems and analyse non-linear processes. The aim of this thesis is to explore ways of modelling adaptive agents in ACE models. Its major contribution is of a methodological nature. Artificial intelligence and machine learning methods are used to represent agents and learning processes in economics domains by means of learning mechanisms. In this work, a general reinforcement learning framework is developed and realised in a simulation system. This system is used to implement three models of increasing complexity in two different economic domains. One of these domains are iterative games in which agents meet repeatedly and interact. In an experimental labour market, it is shown how statistical discrimination can be generated simply by the learning algorithm used. The results resemble actual patterns of observed human behaviour in laboratory settings. The second model treats strategic network formation. The main contribution here is to show how agent-based modelling helps to analyse non-linearity that is introduced when assumptions of perfect information and full rationality are relaxed. The other domain has a Health Economics background. The aim here is to provide insights of how the approach might be useful in real-world applications. For this, a general model of primary care is developed, and the implications of different consumer behaviour patterns (based on the learning features introduced before) analysed.

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Chong, Shi Kai. "A computational approach to urban economics." Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/122318.

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Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2018 Cataloged from PDF version of thesis. Includes bibliographical references (pages 89-92). Cities are home to more than half of the world population today and urbanization is one of this century's biggest drivers of global economic growth. The dynamics of the urban environment is thus an important question to investigate. In this thesis, techniques from statistical modeling, machine learning, data mining and econometrics are utilized to study digital traces of people's everyday lives. In particular, we investigated how people influence the economic growth of cities, as well as how the urban environment affect the decisions made by people. Focusing on the role of cities as centers of consumption, we found that a gravity model based on the availability of a large and diverse pool of amenities accurately explained human flows observed from credit card records. Investigation of the consumption patterns of individuals in Istanbul, Beijing and various metropolitan areas in the United States revealed a positive relationship between the diversity of urban amenities consumed and the city's economic growth. Taking the perspective of cities as hubs for information exchange, we modeled the interactions between individuals in the cities of Beijing and Istanbul using records of their home and work locations and demonstrated how cities which facilitate the mixing of diverse human capital are crucial to the flow of new ideas across communities and their productivity. This contributes to the body of evidence which supports the notion that efficient information exchange is the key factor that drives innovation. To investigate how urban environments shape people's decisions, we study the social influence city dwellers have on each other and showed how face-to-face interaction and information exchange across different residential communities can shape their behavior and increase the similarity of their financial habits and political views in Istanbul. by Shi Kai Chong. S.M. S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program

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GIRIBONE, PIER GIUSEPPE. "Mathematical modeling in Quantitative Finance and Computational Economics." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1046108.

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The first part of my PhD Thesis deals with different Machine Learning techniques mainly applied to solve financial engineering and risk management issues. After a short literary review, every chapter analyzes a particular topic linked to the implementation of these models, showing the most suitable methodologies able to solve it efficiently. The following topics are therefore covered: *) Data Fitting and Regression *) Forecasting *) Classification *) Outlier Detection and Data Quality *) Pricing Every chapter provides the theoretical explanation of the model, the description of the implementation in a numerical computing environment and the solution for real case-studies. Among others, the main technologies discussed in this work are the following: *) Shallow Multi-Layers networks *) Feed-forward and static networks *) Radial Basis Functions (RBF) networks *) Recurrent and Dynamic Neural Networks *) Nonlinear Autoregressive (NAR) networks and Nonlinear Autoregressive networks with exogenous variables (NARX) *) Deep Neural networks *) Convolutional Networks (Conv Net) *) Fuzzy C-Means (FCM) clustering *) Self-Organizing Maps (SOM) and Kohonen networks *) Neural Networks with Circular Neurons *) Auto-Associative Neural Networks (AANN) and Auto-encoders for Nonlinear Principal Component Analysis (NLPCA) The second part of my PhD Thesis deals with the problem of Optimal Control in Quantitative Finance and Labour Economics. Even if the fields of application are hugely different, they share the same mathematical instrument for their solution: the Bellman principle of optimality. After a short literary review that introduces the financial and economic problems solved in this part, the following four chapters show the most popular pricing techniques used to evaluate an option: closed formulas, Partial Differential Equations (PDE), Lattice methods and Stochastic Differential Equations (SDE). Chapter 6 faces the problem of early-exercise in option pricing and shows how to apply the principle of optimality in the models presented in the previous chapters. The following pricing methodologies are covered: *) Stochastic Trees and Lattice models (Cox-Ross-Rubinstein, Tian, Jarrow-Rudd, Drifted CRR, Leisen-Reimer, CRR Trinomial, Adaptive Mesh Method (AMM), Pentanomial and Heptanomial Trees) *) PDE numerical schemes (Finite Difference Method - FDM, Finite Elements Method - FEM and Radial Basis Function - RBF) *) SDE numerical solution (Longstaff-Schwartz Monte Carlo) *) Quasi-closed formulas (Roll-Geske-Whaley, Barone-Adesi-Whaley, Bjerksund- Stensland model) The last two chapters examine two important Labour Economics dynamic problems in the field of Optimal Control Theory: Implicit Contracts and Wage Bargaining. They share the same procedure for the solution which can be synthesized in these steps: *) Infinite-horizon deterministic optimal control problem formulation. The solution for this kind of problem can be found applying the Hamilton – Jacobi – Bellman (HJB) Equation. *) Design of a Markov Decision Chain for the numerical solution of the previous problem. *) Infinite-horizon stochastic optimal control problem formulation. After the validation of the discretization scheme in the deterministic context, the Markov Decision Chain can be extended in order to solve the stochastic version of the problem. In particular, an Ornstein-Uhlenbeck process has been introduced in the model. The third part of my PhD Thesis deals with Forecasting and Risk Management in Energy Markets. The first chapter introduces the two studies presented in this field through a short literary review and the Regulatory framework. The second chapter suggests some quantitative methods with the aim of managing the main risks of Guarantees of Origin (Gos). Given that Gos trading is rather recent, it implements an innovative integrated control system in order to handle market and counterparty risks. The following techniques are covered: *) Market Risk: Historical, parametric and Monte Carlo VaR with a special focus on volatility modeling (historical, implied, GARCH, SABR). *) Liquidity Risk: Bid-Ask spread analysis. *) Counterparty Risk: Probability of Default estimation starting from: listed CDS premium, traded bond prices and statement analysis (KMV model). The third chapter deals with the energy spot prices forecasting problem. The aim of the study is to establish a time-horizon within which it is reasonable to predict prices. The state-of-the-art architectures based on Deep Learning methods are implemented in order to solve this econometric issue. The analyzed techniques are: *) A multi-layered Nonlinear Autoregressive (NAR) network (Endogenous variable: prices). *) A multi-layered Nonlinear Autoregressive with an exogenous variable (NARX) network (Endogenous variable: prices - Exogenous variable: demand). *) A Long Short-Term Memory (LSTM) network with one feature (prices). *) A Long Short-Term Memory (LSTM) network with two features (prices and demand).

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Hull, Isaiah. "Essays in Computational Macroeconomics and Finance." Thesis, Boston College, 2013. http://hdl.handle.net/2345/bc-ir:104376.

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Thesis advisor: Peter N. Ireland This dissertation examines three topics in computational macroeconomics and finance. The first two chapters are closely linked; and the third chapter covers a separate topic in finance. Throughout the dissertation, I place a strong emphasis on constructing computational tools and modeling devices; and using them in appropriate applications. The first chapter examines how a central banks choice of interest rate rule impacts the rate of mortgage default and welfare. In this chapter, a quantitative equilibrium (QE) model is constructed that incorporates incomplete markets, aggregate uncertainty, overlapping generations, and realistic mortgage structure. Through a series of counterfactual simulations, five things are demonstrated: 1) nominal interest rate rules that exhibit cyclical behavior increase the average default rate and lower average welfare; 2) welfare can be substantially improved by adopting a modified Taylor rule that stabilizes house prices; 3) a decrease in the length of the interest rate cycle will tend to increase the average default rate; 4) if the business and housing cycles are not aligned, then aggressive inflation targeting will tend to increase the mortgage default rate; and 5) placing a legal cap on loan-to-value ratios will lower the average default rate and lessen the intensity of extreme events. In addition to these findings, this paper also incorporates an important mechanism for default, which had not pre- viously been included in the QE literature: default spikes happen when income falls and home equity is degraded at the same time. The paper concludes with a policy recommendation for central banks: if they wish to crises where many households default simultaneously, they should either adopt a rule that generates interest rates with slow-moving cycles or use a modified Taylor rule that also targets house price growth. The second chapter generalizes the solution method used in the first and compares it to more common techniques used in the computational macroeconomics literature, including the parameterized expectations approach (PEA), projection methods, and value function iteration. In particular, this chapter compares the speed and accuracy of the aforementioned modifications to an alternative method that was introduced separately by Judd (1998), Sutton and Barto (1998), and Van Roy et al. (1997), but was not developed into a general solution method until Powell (2007) introduced it to the Operations Research literature. This approach involves rewriting the Bellman equation in terms of the post-decision state variables, rather than the pre-decision state variables, as is done in standard dynamic programming applications in economics. I show that this approach yields considerable performance benefits over common global solution methods when the state space is large; and has the added benefit of not forcing modelers to assume a data generating process for shocks. In addition to this, I construct two new algorithms that take advantage of this approach to solve heterogenous agent models. Finally, the third chapter imports the SIR model from mathematical epidemiol- ogy; and uses it to construct a model of financial epidemics. In particular, the paper demonstrates how the SIR model can be microfounded in an economic context to make predictions about financial epidemics, such as the spread of asset-backed securities (ABS) and exchange-traded funds (ETFs), the proliferation of zombie financial institutions, and the expansion of financial bubbles and mean-reverting fads. The paper proceeds by developing the 1-host SIR model for economic and financial contexts; and then moves on to demonstrate how to work with the multi-host version of the model. In addition to showing how the SIR framework can be used to model economic interactions, it will also: 1) show how it can be simulated; 2) use it to develop and estimate a sufficient statistic for the spread of a financial epidemic; and 3) show how policymakers can impose the financial analog of herd immunity-that is, prevent the spread of a financial epidemic without completely banning the asset or behavior associated with the epidemic. Importantly, the paper will focus on developing a neutral framework to describe financial epidemics that can be either bad or good. That is, the general framework can be applied to epidemics that constitute a mean-reverting fad or an informational bubble, but ultimately yield little value and shrink in importance; or epidemics that are long-lasting and yield a new financial in- strument that generates permanent efficiency gains or previously unrealized hedging opportunities Thesis (PhD) — Boston College, 2013 Submitted to: Boston College. Graduate School of Arts and Sciences Discipline: Economics

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Wong, Yiu Kwong. "Application of computational models and qualitative reasoning to economics." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/688.

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Lupi, Paolo. "The evolution of collusion : three essays in computational economics." Thesis, University of York, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341598.

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Libri sul tema "Computational economics"

Kendrick, David A. Computational economics. Princeton, N.J: Princeton University Press, 2006.

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Ruben, Mercado P., and Amman Hans M, eds. Computational economics. Princeton: Princeton University Press, 2006.

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Tesfatsion, Leigh. Handbook of Computational Economics: Agent-Based Computational Economics. Burlington: Elsevier, 2006.

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1959-, Chen Shu-Heng, Jain L. C, and Tai Chung-Ching, eds. Computational economics: A perspective from computational intelligence. Hershey PA: Idea Group Pub., 2006.

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M, Amman Hans, Belsley David A, and Pau L. F. 1948-, eds. Computational economics and econometrics. Dordrecht: Kluwer Academic Publishers, 1992.

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Amman, Hans M., David A. Belsley, and Louis F. Pau, eds. Computational Economics and Econometrics. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-3162-9.

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Varian, Hal R., ed. Computational Economics and Finance. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2340-5.

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M, Amman Hans, Kendrick David A, and Rust John 1955-, eds. Handbook of computational economics. Amsterdam: Elsevier, 1996.

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Cooper, W. W., and A. B. Whinston, eds. New Directions in Computational Economics. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0770-9.

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1914-, Cooper William W., and Whinston Andrew B, eds. New directions in computational economics. Dordrecht: Kluwer Academic Publishers, 1994.

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Capitoli di libri sul tema "Computational economics"

Chen, Shu-Heng. "Computational evolutionary economics." In Routledge Handbook of Evolutionary Economics, 147–60. London: Routledge, 2023. http://dx.doi.org/10.4324/9780429398971-13.

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Andrášik, Ladislav. "Computational Qualitative Economics." In Computational Intelligence in Engineering, 247–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15220-7_20.

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Marino, Domenico. "Regional Economic Policy and Computational Economics." In Neural Nets WIRN Vietri-99, 376–90. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0877-1_43.

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Levy, Moshe. "Agent Based Computational Economics." In Computational Complexity, 18–38. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1800-9_2.

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Herbert, Ric D. "Computational Programming Environments." In Advances in Computational Economics, 271–96. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1049-9_10.

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Levy, Moshe. "Agent Based Computational Economics." In Complex Systems in Finance and Econometrics, 1–21. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-7701-4_1.

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Levy, Moshe. "Agent-Based Computational Economics." In Encyclopedia of Complexity and Systems Science, 1–30. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-3-642-27737-5_6-7.

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Levy, Moshe. "Agent Based Computational Economics." In Encyclopedia of Complexity and Systems Science, 92–112. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_6.

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Levy, Moshe. "Agent-Based Computational Economics." In Complex Social and Behavioral Systems, 825–49. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0368-0_6.

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Chen, Shu-Heng. "Computational Intelligence in Agent-Based Computational Economics." In Studies in Computational Intelligence, 517–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78293-3_13.

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Atti di convegni sul tema "Computational economics"

Zhang, Haobo. "Research on Regional Economic Development and Application of Computational Economics Method." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC), 1–5. IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872237.

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Guo, Yue, and Yi Yang. "EconNLI: Evaluating Large Language Models on Economics Reasoning." In Findings of the Association for Computational Linguistics ACL 2024, 982–94. Stroudsburg, PA, USA: Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-acl.58.

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Halpern, Joseph Y., Rafael Pass, and Lior Seeman. "Computational Extensive-Form Games." In EC '16: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2940716.2940733.

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Yang, Jianhong. "Parametricization of Language Economics in Computational Advertising." In ICEIT 2019: 2019 8th International Conference on Educational and Information Technology. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3318396.3318397.

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Criner, O. "Control systems identification in finance and economics." In COMPUTATIONAL FINANCE 2008. Southampton, UK: WIT Press, 2008. http://dx.doi.org/10.2495/cf080011.

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Hansen, Kristoffer Arnsfelt, and Troels Bjerre Lund. "Computational Complexity of Proper Equilibrium." In EC '18: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219166.3219199.

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Cai, Yang, and Christos Papadimitriou. "Simultaneous bayesian auctions and computational complexity." In EC '14: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600057.2602877.

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Echenique, Federico, Daniel Golovin, and Adam Wierman. "A revealed preference approach to computational complexity in economics." In the 12th ACM conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993574.1993591.

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Hu, Chenrui, and Ming Zhang. "Fractional High Frequency Cosine and Sine Higher Order Neural Network for Economics." In 2019 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2019. http://dx.doi.org/10.1109/csci49370.2019.00102.

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SANDHOLM, THOMAS, and KEVIN LAI. "EVALUATING DEMAND PREDICTION TECHNIQUES FOR COMPUTATIONAL MARKETS." In Proceedings of the 3rd International Workshop on Grid Economics and Business Models. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773470_0001.

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Rapporti di organizzazioni sul tema "Computational economics"

Judd, Kenneth. Computational Economics and Economic Theory: Substitutes or Complements. Cambridge, MA: National Bureau of Economic Research, February 1997. http://dx.doi.org/10.3386/t0208.

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Hayashi, Tadateru, Sanchita Basu Das, Manbar Singh Khadka, Ikumo Isono, Souknilanh Keola, Kenmei Tsubota, and Kazunobu Hayakawa. Economic Impact Analysis of Improved Connectivity in Nepal. Asian Development Bank, November 2020. http://dx.doi.org/10.22617/wps200312-2.

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This study estimates and analyzes the economic impact of ongoing and future infrastructure development projects in Nepal by using the geographical simulation model developed by the Institute of Developing Economies (IDE-GSM). The IDE-GSM is a computational general equilibrium model based on spatial economics. The simulation analysis reveals that ongoing infrastructure development projects in Nepal benefit the country’s economy, and that the planned connectivity improvement with India will have positive impact with anticipated major shift in mode of transport for trade. The study takes into consideration efforts by the Government of Nepal to promote and strengthen international connectivity under the South Asia Subregional Economic Cooperation framework.

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Alhaji, Mohammed M., Nyaga Robert, and Patrick Forscher. All responses are local. How behavioral systems can enhance global management of the Mpox outbreak. Busara, October 2024. http://dx.doi.org/10.62372/yeup3422.

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Mpox represents a dire threat to public health. We believe that behavioral science can play a crucial role in understanding and managing the Mpox threat as effective management of public health requires understanding and managing human behavior. However, behavioral science alone may not be enough: pandemics spread through complex networks of human behavior that we call behavioral systems (Diaz del Valle, Wendel, & Jang, 2023); responding effectively to Mpox may therefore require an interdisciplinary toolkit that integrates qualitative and quantitative methods from across psychology, behavioral economics, anthropology, political science, systems analysis, and computational methods to understand how individual behaviors fit within larger systems, and how these systems can, in turn, be changed through effective targeting of behavioral interventions within them.

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Diakonova, Marina, Corinna Ghirelli, and Juan Quiñónez. Economic Policy Uncertainty in Central America and the Dominican Republic. Madrid: Banco de España, August 2024. http://dx.doi.org/10.53479/37524.

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The advent of Big Data and computational tools has transformed macroeconomic analysis, introducing real-time, high-frequency text-based indicators such as the economic policy uncertainty (EPU) index pioneered by Baker et al. (2016). However, constructing the EPU index for developing economies remains a challenge, mostly due to limited press coverage. Our study focuses on the Central American region, comprising Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama and the Dominican Republic (CAPADR). We construct country-specific EPU indices using a combination of local and regional sources and validate them using the narrative approach in order to ensure variation accurately reflects relevant economic policy events. We offer further empirical validation by computing impulse response functions for key macroeconomic variables, at both country and representative country level. We show that EPU shocks lead to a decline in economic activity, foreign direct investment (FDI) and tourism levels. Our findings underline the importance of EPU monitoring in Central America and offer a solution through our indices.

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Mermelstein, Ben, Volker Nocke, Mark Satterthwaite, and Michael Whinston. Internal versus External Growth in Industries with Scale Economies: A Computational Model of Optimal Merger Policy. Cambridge, MA: National Bureau of Economic Research, April 2014. http://dx.doi.org/10.3386/w20051.

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Pearson, Ken, and Channing Arndt. Implementing Systematic Sensitivity Analysis Using GEMPACK. GTAP Technical Paper, November 2000. http://dx.doi.org/10.21642/gtap.tp03.

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In economic simulation, results often hinge crucially on values of key exogenous inputs (the values of the parameters of the model and the shocks applied). Computational burden has, in the past, hindered systematic investigation of the impacts of variations in these key exogenous inputs. In this document, practical methods for conducting systematic sensitivity analysis for any model implemented using the GEMPACK suite of software are documented. The procedures described here are based on GTAP Technical Paper number 2 which sets out the theory behind the Gaussian quadrature methods on which the automated procedure is based. The procedures allow modellers to obtain estimates of the means and standard deviations of any endogenous variables of their model. The model only needs to be solved a relatively modest number of times (usually only 2N times if N exogenous inputs are varying); this is considerably fewer than the number of solves required by Monte Carlo methods. The procedure documented here fully automates solving the model as often as is necessary; once the user sets it up and starts it running, no further intervention is required. The document spells out the assumptions which must be made about the distribution of the exogenous inputs for the methods described to be valid. Five examples of systematic sensitivity computations are presented and the accompanying software allows modellers to work through these examples while reading the document. This should leave readers fully prepared to analyse the sensitivity of results for any model implemented in GEMPACK.

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Danylchuk, Hanna B., and Serhiy O. Semerikov. Advances in machine learning for the innovation economy: in the shadow of war. Криворізький державний педагогічний університет, August 2023. http://dx.doi.org/10.31812/123456789/7732.

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This preface introduces the selected and revised papers presented at the 10th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2022), held online in Ukraine, on November 17-18, 2022. The conference aimed to bring together researchers, practitioners, and students from various fields to exchange ideas, share experiences, and discuss challenges and opportunities in applying computational intelligence and data science for the innovation economy. The innovation economy is a term that describes the emerging paradigm of economic development that is driven by knowledge, creativity, and innovation. It requires new approaches and methods for solving complex problems, discovering new opportunities, and creating value in various domains of science, business,and society. Computational intelligence and data science are two key disciplines that can provide such approaches and methods by exploiting the power of data, algorithms, models, and systems to enable intelligent decision making, learning, adaptation, optimization, and discovery. The papers in this proceedings cover a wide range of topics related to computational intelligence and data science for the innovation economy. They include theoretical foundations, novel techniques, and innovative applications. The papers were selected and revised based on the feedback from the program committe members and reviewers who ensured their high quality. We would like to thank all the authors who submitted their papers to M3E2 2022. We also appreciate the keynote speakers who shared their insights and visions on the current trends and future directions of computational intelligence and data science for the innovation economy. We acknowledge the support of our sponsors, partners, and organizers who made this conference possible despite the challenging circumstances caused by the ongoing war in Ukraine. Finally, we thank all the participants who attended the conference online and contributed to its success.

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Korinek, Anton, and Jai Vipra. Concentrating Intelligence: Scaling and Market Structure in Artificial Intelligence. Institute for New Economic Thinking Working Paper Series, October 2024. http://dx.doi.org/10.36687/inetwp228.

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This paper examines the evolving structure and competition dynamics of the rapidly growing market for foundation models, focusing on large language models (LLMs). We describe the technological characteristics that shape the industry and have given rise to fierce competition among the leading players. The paper analyzes the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data, and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration in the future. We explore two concerns for competition, the risk of market tipping and the implications of vertical integration, and use our analysis to inform policy remedies to maintain a competitive landscape.

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Moreno Pérez, Carlos, and Marco Minozzo. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23646.

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This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.

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Tzonev, Nick. PR-396-183905-R01 Autonomous System For Monitoring Pipeline River Crossings. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2021. http://dx.doi.org/10.55274/r0012110.

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The goal of the GHZ-2-01 Project is to develop and lab-test a system for monitoring underground pipeline facilities at remote river crossings where access to power and wireline communications is not readily available. A next generation real-time river crossing monitoring solution requires an integration of various sensor types, data computation capabilities, and low power wireless connectivity which would: - utilize proven sensors technologies such as accelerometers, inclinometer strings and float-out buoys to detect dangerous conditions, - be able to recognize and minimize false alarms by examining a combination of sensors, - alarm on contact with hydrocarbons, - require minimal maintenance, - be easily scalable, both geographically and as a network, - provide seamless integration into Supervisory Control and Acquisition (SCADA) systems, and - be economical. There is a related webinar.

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