Ruth Meyer

Simulating Social Complexity

ion can be defined as “ignoring or hiding details to capture some kind of commonality between different instances” (Free On-line Dictionary of Computing). 26 Human Societies: Understanding Observed Social Phenomena 713

Event-Driven Multi-agent Simulation

Most agent-based models today apply a time-driven approach, i.e. simulation time is advanced in equidistant steps. This time advance method is considerably easier to implement than the more flexible and efficient event-driven approach.

Informal Approaches to Developing Simulation Models

This chapter describes an approach commonly taken by most people in the social sciences when developing simulation models instead of following a formal approach of specification, design and implementation. What often seems to happen in practice is that modellers start off in a phase of exploratory modelling, where they don’t have a precise conception of the model they want but a series of ideas and/or evidence they want to capture. They then may develop the model in different directions, backtracking and changing their ideas as they go. This phase continues until they think they may have a model or results that are worth telling others about. This then is (or at least should be) followed by a consolidation phase where the model is more rigorously tested and checked so that reliable and clear results can be reported. In a sense what happens in this later phase is that the model is made so that it is as if a more formal and planned approach had been taken.

DRAMS - A Declarative Rule-Based Agent Modelling System.

This paper presents a snapshot of the Declarative Rulebased Agent Modelling System (DRAMS), which is currently being developed with the goal of providing declarative rule engine functionality to agent-based simulation models. While the incentive for this effort is to allow stakeholders in policy modelling activities to better understand and, hence, to be more deeply involved in modelling and simulation of policy processes, the approach chosen appears to be valuable for many other areas. After a short literature review on similar approaches, the paper briefly outlines the FP7 project OCOPOMO, the context in which DRAMS is developed, in order to underpin some of the requirements and related design decisions presented subsequently. On the basis of this information, the software architecture is described in some detail, followed by a few notes about implementation and user interfaces. Finally, a small “toy” model illustrates the use of DRAMS.

Comparing Two Sexual Mixing Schemes for Modelling the Spread of HIV/AIDS

In this paper we compare the impact of two sexual mixing schemes on the characteristics of the resulting sexual networks and the spread of HIV. This work is part of our studying social complexity in the Sekhukhune district of the Limpopo province in South Africa. While the agent-based models are constrained by evidence wherever possible, little or no evidence is available about individuals’ choice of partners in the region and their sexual behaviour. Since we therefore have to depend on plausible assumptions we decided to study different sexual mixing schemes and their effect on the formation of sexual networks. We report on some fundamental network signatures and discuss the resulting HIV/AIDS prevalence as a macro-level output of the simulation.

Introduction to the Handbook

To understand some of the background and motivation for the handbook and how it is structured.

Structural Changes in Agent-Based Simulations: Representing HIV/AIDS Impact on Social Networks

HIV/AIDS in the Sub-Saharan Africa is one of the biggest threats against sustainable human development in the region. One of the implications of this epidemic is that it not only affects individuals and their households but also increases burden on traditional social and support networks or safety nets. Research reported in recent years, have indicated on the possibility of weakening and even breaking of these support networks. In this paper, we suggest that by analyzing dynamical networks generated from agent-based simulations, one could describe this effect with better precision. This idea is based upon our previous work, which attempts at finding techniques to identify structural changes in dynamic networks.