The Evolution of Social and Economic Networks

Preface (L. Tesfatsion, K.L. Judd). Part 1. ACE research reviews. 1. Agent-based computational economics: A constructive approach to economic theory (L. Tesfatsion). 2. Computationally intensive analyses in economics (K.L. Judd). 3. Agent learning representation: Advice on modelling economic learning (T. Brenner). 4. Agent-based models and human subject experiments (J. Duffy). 5. Economic activity on fixed networks (A.W. Wilhite). 6. ACE models of endogenous interactions (N.J. Vriend). 7. Social dynamics: Theory and applications (H. Peyton Young). 8. Heterogeneous agent models in economics and finance (C. Hommes). 9. Agent-based computational finance (B. LeBaron). 10. Agent-based models of innovation and technological change (H. Dawid). 11. Agent-based models of organizations (M-H. Chang, J.E. Harrington, Jr.). 12. Market design using agent-based models (R. Marks). 13. Automated markets and trading agents (J.J. MacKie-Mason, M. Wellman). 14. Computational methods and models of politics (K. Kollman, S.E. Page). 15. Governing social-ecological systems (M.A. Janssen, E. Ostrom). 16. Computational laboratories for spatial agent-based models (C. Dibble). Part 2. Perspectives on the ACE methodology. Out-of-equilibrium economics and agent-based modeling (W.B. Arthur). Agent-based modeling as a bridge between disciplines (R. Axelrod). Remarks on the foundations of agent-based generative social science (J.M. Epstein). Coordination issues in long-run growth (P. Howitt). Agent-based macro (A. Leijonhufvud). Some fun, thirty-five years ago (T.C. Schelling). Part 3. Guideline for newcomers to agent-based modeling. A guide for newcomers to agent-based modeling in the social sciences (R. Axelrod, L. Tesfatsion).

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Agent-based computational economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.

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Agent-Based Computational Economics: Growing Economies From the Bottom Up

http://media.terry.uga.edu/documents/finance/decision_making.pdf

Multiple criteria decision making combined with finance: A categorized bibliographic study

In this chapter, I provide an overview of research on social networks and their role in shaping behavior and economic outcomes. I include discussion of empirical and theoretical analyses of the role of social networks in markets and exchange, learning and diffusion, and network games. I also include some background on social network characteristics and measurements, models of network formation, models for the statistical analysis of social networks, as well as community detection. JEL Classification Codes: D85, C72, L14, Z13

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An Overview of Social Networks and Economic Applications

Recognizing that robust decision making is vital in risk management, this book provides concepts and algorithms for computing the best decision in view of the worst-case scenario. The main tool used is minimax, which ensures robust policies with guaranteed optimal performance that will improve further if the worst case is not realized. The applications considered are drawn from finance, but the design and algorithms presented are equally applicable to problems of economic policy, engineering design, and other areas of decision making.Critically, worst-case design addresses not only Armageddon-type uncertainty. Indeed, the determination of the worst case becomes nontrivial when faced with numerous--possibly infinite--and reasonably likely rival scenarios. Optimality does not depend on any single scenario but on all the scenarios under consideration. Worst-case optimal decisions provide guaranteed optimal performance for systems operating within the specified scenario range indicating the uncertainty. The noninferiority of minimax solutions--which also offer the possibility of multiple maxima--ensures this optimality.Worst-case design is not intended to necessarily replace expected value optimization when the underlying uncertainty is stochastic. However, wise decision making requires the justification of policies based on expected value optimization in view of the worst-case scenario. Conversely, the cost of the assured performance provided by robust worst-case decision making needs to be evaluated relative to optimal expected values.Written for postgraduate students and researchers engaged in optimization, engineering design, economics, and finance, this book will also be invaluable to practitioners in risk management.

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Algorithms for Worst-Case Design and Applications to Risk Management

Section One:- Factor-GARCH Modeling of the Treasury Term Structure C.F. Baum, B. Bekdache. Analyzing a Small French ECM Model J.-L. Brillet. Wavelet Basis Selection for Regression by Cross-Validation S.A. Greenblatt. Computation and Inference in Semiparametric Efficient Estimation R.M. Adams, et al. Generating Random Numbers in Mathematica D.A. Belsley. Linked-Cone Profit Ratio Estimates of U.S. Total Factor Productivity Growth, Using DEA/AR Methods R.G. Thompson, et al. Several Algorithms to Determine Multipliers for Use in Cone-Ratio Envelopment Approaches to Efficiency Evaluations in DEA K. Tone. Section Two:- The Estimation of the Heath-Jarrow-Morton Model by Use of Kalman Filtering Techniques R. Bhar, C. Chiarella. Neural Networks for Contingent Claim Pricing via the Galerkin Method E. Barucci, et al. Asset Liability Management Diem Ho. An Efficient Parallel Implementation of a Lattice Pricing Model S.S. Nielsen, et al. Monitoring Active Portfolios Using Statistical Process Control E. Yashchin, et al. Section Three:- Ordering: Human Versus Computer A. Norman, et al. Strategic Uncertainty and the Genetic Algorithm Adaptation J. Arifovic. Fluctuating Benefits and Collective Action B.A. Huberman. A Trade Network Game with Endogenous Partner Selection L. Tesfatsion. Learning in a Computable Setting. Applications of Gold's Inductive Inference Model F. Luna. Section Four:- The Range Process in Random Walks: Theoretical Results and Applications P. Vallois, C.S. Tapiero. Numerical Analysis of a Nonlinear Operator Equation Arising from a Monetary Model J. Li. A Numerical Procedure to Estimate Real Business Cycle Models Using Simulated Annealing W. Semmler, Gang Gong. Section Five:- The Internet: A Future Tragedy of the Commons? A. Gupta, et al. The DUALI/DUALPC Software for Optimal Control Models: Introduction H.M. Amman, D.A. Kendrick.

Computational approaches to economic problems

Optimization of a single objective quadratic programming algorithms interactive search for acceptable decisions - updating quadratic objective weights, updating bliss points and arbitrariness of shadow prices, convergence and complexity of decision processes nonlinear optimization with convex constraints - the Goldstein-Levitin-Polyak algorithm nonlinear optimization with equality and inequality constraints convergence rates of SQP algorithms algorithms for equilibria and games uncertainty mean versus variance optimization - the multi-currency portfolio the nonlinear case uncertainty with multiple scenarios - discrete min-max algorithm for equality constraints.

Algorithms for Nonlinear Programming and Multiple-Objective Decisions

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Computational methods in decision-making, economics and finance

Nonlinearly constrained optimisation techniques based on projections.- Projection methods for computing feasible points of linearly constrained regions.- An algorithm for positive definite quadratic programming.- Optimisation with linear and nonlinear constraints.- The iterative specification of objective functions in economic policy optimisation: An application of projection methods.- Policy optimisation algorithms for nonlinear econometric models.- Suggestions for further research.

Berçch Rüstem

Papers

Algorithms for Worst-Case Design and Applications to Risk Management

Computational approaches to economic problems

Algorithms for Nonlinear Programming and Multiple-Objective Decisions

Computational methods in decision-making, economics and finance

Projection Methods in Constrained Optimisation and Applications to Optimal Policy Decisions