Preface. Economic Topics. Computable general equilibrium modelling for policy analysis and forecasting (P.B. Dixon, B.R. Parmenter). Computation of equilibria in finite games (R.D. McKelvey, A. McLennan). Computational methods for macroeconomic models (R.C. Fair). Mechanics of forming and estimating dynamic linear economies (E.W. Andersen et al.). Nonlinear pricing and mechanism design (R. Wilson). Sectoral economics (D.A. Kendrick). Computer Science Parallel computation (A. Nagurney). Artificial intelligence in economics and finance: A state of the art - 1994 (L.F. Pau, P.-Y. Tan). Neural networks for encoding and adapting in dynamic economies (I.-K. Cho, T.J. Sargent). Modelling languages in computational economics: GAMS (S.A. Zenios). Numerical Methods. Mathematica for economists (H. Varian). Approximation, perturbation, and projection methods in economic analysis (K.L. Judd). Numerical methods for linear-quadratic models (H.M. Amman). Numerical dynamic programming in economics (J. Rust). Monte Carlo simulation and numerical integration (J. Geweke).

/pdf/handbook-of-computational-economics-531sv06e9a.pdf

Handbook of Computational Economics

Supply chain risk management is a large and growing field of research. However, within this field, mathematical models for agricultural products have received relatively little attention. This is somewhat surprising as risk management is even more important for agricultural supply chains due to challenges associated with seasonality, supply spikes, long supply lead-times, and perishability. This paper carries out a thorough review of the relatively limited literature on quantitative risk management models for agricultural supply chains. Specifically, we identify robustness and resilience as two key techniques for managing risk. Since these terms are not used consistently in the literature, we propose clear definitions and metrics for these terms; we then use these definitions to classify the agricultural supply chain risk management literature. Implications are given for both practice and future research on agricultural supply chain risk management.

/pdf/agribusiness-supply-chain-risk-management-a-review-of-2g0eu893my.pdf

Agribusiness supply chain risk management: A review of quantitative decision models

The life course cube: A tool for studying lives

Since the advent of computers, the natural and engineering sciences have enormously progressed. Computer simulations allow one to understand interactions of physical particles and make sense of astronomical observations, to describe many chemical properties ab initio, and to design energy-efficient aircrafts and safer cars. Today, the use of computational devices is pervasive. Offices, administrations, financial trading, economic exchange, the control of infrastructure networks, and a large share of our communication would not be conceivable without the use of computers anymore. Hence, it would be very surprising, if computers could not make a contribution to a better understanding of social and economic systems.

Agent-Based Modeling

The application of agent-based modelling (ABM) to simulating dynamics within geographical systems has seen a considerable increase over the last decade. ABM allows the disaggregation of systems into individual components that can potentially have their own characteristics and rule sets. This is a powerful paradigm that can be exploited through simulation to further our knowledge of the workings of geographical systems. We present in this chapter an overview of ABM; the main features of an agent-based model are given, along with a discussion of what constitutes an agent-based model. The distinction between cellular automata (CA), microsimulation (MSM) and agent-based models are discussed along with the advantages and limitations of ABM for modelling geographical systems. We conclude with a brief discussion of important areas for further research.

Introduction to Agent-Based Modelling

Series Editor's Introduction Preface Acknowledgments 1. The Idea of Agent-Based Modeling 1.1 Agent-Based Modeling 1.2 Some Examples 1.3 The Features of Agent-Based Modeling 1.4 Other Related Modeling Approaches 2. Agents, Environments, and Timescales 2.1 Agents 2.2 Environments 2.3 Randomness 2.4 Time 3. Using Agent-Based Models in Social Science Research 3.1 An Example of Developing an Agent-Based Model 3.2 Verification: Getting Rid of the Bugs 3.3 Validation 3.4 Techniques for Validation 3.5 Summary 4. Designing and Developing Agent-Based Models 4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments 4.2 Using NetLogo to Build Models 4.3 Building the Collectivities Model Step by Step 4.4 Planning an Agent-Based Model Project 4.5 Reporting Agent-Based Model Research 4.6 Summary 5. Advances in Agent-Based Modeling 5.1 Geographical Information Systems 5.2 Learning 5.3 Simulating Language Resources Glossary References Index About the Author

What are agent-based models?

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What are agent-based models?