https://bio.uib.no/te/papers/Grimm_2010_The_ODD_protocol_.pdf

The ODD protocol: A review and first update

I What Are the Ages of My Three Sons?.- 1 Why Are Some Problems Difficult to Solve?.- II How Important Is a Model?.- 2 Basic Concepts.- III What Are the Prices in 7-11?.- 3 Traditional Methods - Part 1.- IV What Are the Numbers?.- 4 Traditional Methods - Part 2.- V What's the Color of the Bear?.- 5 Escaping Local Optima.- VI How Good Is Your Intuition?.- 6 An Evolutionary Approach.- VII One of These Things Is Not Like the Others.- 7 Designing Evolutionary Algorithms.- VIII What Is the Shortest Way?.- 8 The Traveling Salesman Problem.- IX Who Owns the Zebra?.- 9 Constraint-Handling Techniques.- X Can You Tune to the Problem?.- 10 Tuning the Algorithm to the Problem.- XI Can You Mate in Two Moves?.- 11 Time-Varying Environments and Noise.- XII Day of the Week of January 1st.- 12 Neural Networks.- XIII What Was the Length of the Rope?.- 13 Fuzzy Systems.- XIV Everything Depends on Something Else.- 14 Coevolutionary Systems.- XV Who's Taller?.- 15 Multicriteria Decision-Making.- XVI Do You Like Simple Solutions?.- 16 Hybrid Systems.- 17 Summary.- Appendix A: Probability and Statistics.- A.1 Basic concepts of probability.- A.2 Random variables.- A.2.1 Discrete random variables.- A.2.2 Continuous random variables.- A.3 Descriptive statistics of random variables.- A.4 Limit theorems and inequalities.- A.5 Adding random variables.- A.6 Generating random numbers on a computer.- A.7 Estimation.- A.8 Statistical hypothesis testing.- A.9 Linear regression.- A.10 Summary.- Appendix B: Problems and Projects.- B.1 Trying some practical problems.- B.2 Reporting computational experiments with heuristic methods.- References.

/pdf/how-to-solve-it-modern-heuristics-1pu40hrf57.pdf

How to Solve It: Modern Heuristics

Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

/pdf/tutorial-on-agent-based-modelling-and-simulation-4xgz9pwney.pdf

Tutorial on agent-based modelling and simulation

Individual-based modeling is a new, exciting discipline that allows ecologists to explore, using computer simulations, how properties of populations and ecosystems might evolve from the characteristics and behaviors of individual organisms. Individual-based Modeling and Ecology, written by Volker Grimm and Steven F. Railsback, gives an excellent introduction and overview of this field. It should be read by everyone interested in individual-based modeling, and especially by anyone contemplating developing, or being involved with a group developing, an individualbased model.

http://www.gbv.de/dms/weimar/toc/396272002_toc.pdf

Individual-based modeling and ecology

Stakeholder engagement, collaboration, or participation, shared learning or fact-finding, have become buzz words and hardly any environmental assessment or modelling effort today can be presented without some kind of reference to stakeholders and their involvement in the process. This is clearly a positive development, but in far too many cases stakeholders have merely been paid lip service and their engagement has consequentially been quite nominal. Nevertheless, it is generally agreed that better decisions are implemented with less conflict and more success when they are driven by stakeholders, that is by those who will be bearing their consequences. Participatory modelling, with its various types and clones, has emerged as a powerful tool that can (a) enhance the stakeholders knowledge and understanding of a system and its dynamics under various conditions, as in collaborative learning, and (b) identify and clarify the impacts of solutions to a given problem, usually related to supporting decision making, policy, regulation or management. In this overview paper we first look at the different types of stakeholder modelling, and compare participatory modelling to other frameworks that involve stakeholder participation. Based on that and on the experience of the projects reported in this issue and elsewhere, we draw some lessons and generalisations. We conclude with an outline of some future directions.

Position Paper: Modelling with stakeholders

Swarm is a multi-agent software platform for the simulation of complex adaptive systems. In the Swarm system the basic unit of si8mulation is the swarm, a collection of agents executing a schedule of actions. Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms of other agents in nested structures. Swarm provides object oriented libraries of reusable components for building models and analyzing, displaying, and controlling experiments on those models. Swarm is currently available as a beta version in full, free source code form. It requires the GNU C Compiler, Unix, and X Windows. More information about Swarm can be obtained from our web pages, [ Swarm ].

Nelson Minar

Papers

The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations