JASSS logo


(7 articles matched your search)

A Spatial Approach to Network Generation for Three Properties: Degree Distribution, Clustering Coefficient and Degree Assortativity

Jennifer Badham and Rob Stocker
Journal of Artificial Societies and Social Simulation 13 (1) 11

Abstract: Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.

Explanation in Agent-Based Modelling: Functions, Causality or Mechanisms?

Corinna Elsenbroich
Journal of Artificial Societies and Social Simulation 15 (3) 1

Abstract: What kind of knowledge can we obtain from agent-based models? The claim that they help us to study the social world needs unpacking. I will defend agent-based modelling against a recent criticism that undermines its potential as a method to investigate underlying mechanisms and provide explanations of social phenomena. I show that the criticism is unwarranted and the problem can be resolved with an account of explanation that is associated with the social sciences anyway, the mechanism account of explanation developed in Machamer et al. (2000). I finish off discussing the mechanism account with relation to prediction in agent-based modelling.

Put Your Money Where Your Mouth Is: DIAL, A Dialogical Model for Opinion Dynamics

Piter Dykstra, Corinna Elsenbroich, Wander Jager, Gerard Renardel de Lavalette and Rineke Verbrugge
Journal of Artificial Societies and Social Simulation 16 (3) 4

Abstract: We present DIAL, a model of group dynamics and opinion dynamics. It features dialogues, in which agents gamble about reputation points. Intra-group radicalisation of opinions appears to be an emergent phenomenon. We position this model within the theoretical literature on opinion dynamics and social influence. Moreover, we investigate the effect of argumentation on group structure by simulation experiments. We compare runs of the model with varying influence of the outcome of debates on the reputation of the agents.

An Agent-Based Dialogical Model with Fuzzy Attitudes

Piter Dykstra, Wander Jager, Corinna Elsenbroich, Rineke Verbrugge and Gerard Renardel de Lavalette
Journal of Artificial Societies and Social Simulation 18 (3) 3

Abstract: This paper presents an extension to an agent-based model of opinion dynamics built on dialogical logic DIAL. The extended model tackles a pervasive problem in argumentation logics: the difference between linguistic and logical inconsistency. Using fuzzy logic, the linear ordering of opinions, used in DIAL, is replaced by a set of partial orderings leading to a new, nonstandard notion of consistency as convexity of sets of statements. DIAL allows the modelling of the interplay of social structures and individual beliefs, inspired by the interplay between the importance and the evidence of an opinion formulated in the Theory of Reasoned Action, but DIAL was restricted to argumentation about one proposition. FUZZYDIAL allows for a more natural structure of dialogues by allowing argumentation about positions in a multidimensional space.

The Extortion Relationship: A Computational Analysis

Corinna Elsenbroich and Jennifer Badham
Journal of Artificial Societies and Social Simulation 19 (4) 8

Abstract: Systematic extortion involves a long term parasitic relationship between the criminal and the victim. Game theory analysis has provided insight into the choices of individual hypothetical criminal and victim pairs. In this paper we present an agent-based model so as to extend the analysis to the relationship between extorters and other potential victims. The model is developed in two stages, the first to be closest to game theory, the second one making the decision informed by the social environment of the victim. The agent-based model shows the importance of social aspects for the functioning of extortion rackets.

Calibrating with Multiple Criteria: A Demonstration of Dominance

Jennifer Badham, Chipp Jansen, Nigel Shardlow and Thomas French
Journal of Artificial Societies and Social Simulation 20 (2) 11

Abstract: Pattern oriented modelling (POM) is an approach to calibration or validation that assesses a model using multiple weak patterns. We extend the concept of POM, using dominance to objectively identify the best parameter candidates. The TELL ME agent-based model is used to demonstrate the approach. This model simulates personal decisions to adopt protective behaviour during an influenza epidemic. The model fit is assessed by the size and timing of maximum behaviour adoption, as well as the more usual criterion of minimising mean squared error between actual and estimated behaviour. The rigorous approach to calibration supported explicit trading off between these criteria, and ultimately demonstrated that there were significant flaws in the model structure.

Justified Stories with Agent-Based Modelling for Local COVID-19 Planning

Jennifer Badham, Pete Barbrook-Johnson, Camila Caiado and Brian Castellani
Journal of Artificial Societies and Social Simulation 24 (1) 8

Abstract: This paper presents JuSt-Social, an agent-based model of the COVID-19 epidemic with a range of potential social policy interventions. It was developed to support local authorities in North East England who are making decisions in a fast moving crisis with limited access to data. The proximate purpose of JuSt-Social is description, as the model represents knowledge about both COVID-19 transmission and intervention effects. Its ultimate purpose is to generate stories that respond to the questions and concerns of local planners and policy makers and are justified by the quality of the representation. These justified stories organise the knowledge in way that is accessible, timely and useful at the local level, assisting the decision makers to better understand both their current situation and the plausible outcomes of policy alternatives. JuSt-Social and the concept of justified stories apply to the modelling of infectious disease in general and, even more broadly, modelling in public health, particularly for policy interventions in complex systems.