JASSS logo


(4 articles matched your search)

Teaching Social Simulation with Matlab

Warren Thorngate
Journal of Artificial Societies and Social Simulation 3 (1) forum/1

Abstract: Programming languages for social simulations are rapidly proliferating. The result is a Tower of Babel effect: Many of us find it increasingly effortful to learn and to teach more programming languages and increasingly difficult to sustain an audience beyond the programming dialect of our choice. We need a programming lingua franca. Here I argue why Matlab might be worth our consideration, especially to teach simulation programming techniques.

Sentiment and Social Mitosis: Implications of Heider's Balance Theory

Zhigang Wang and Warren Thorngate
Journal of Artificial Societies and Social Simulation 6 (3) 2

Abstract: Two Monte Carlo simulations were developed to investigate the social consequences of balancing sentiment relations among triads of members of a larger group, when balancing one triad can imbalance others. Using assumptions of Balance Theory (Heider, 1958), random starting combinations of liking, disliking and no relations among 9 or16 people were iteratively adjusted to determine if the relations ever settled to a steady state and what subgroups might emerge. Results show that, regardless of the starting configuration of sentiments, all imbalances in a group are eventually balanced in a steady state containing no more than two subgroups. Two subgroups are the rule; their relative size depends on the starting number of positive, negative and null relations. Members within each subgroup are linked by positive relations (liking), and show only negative relations (disliking) towards members of the other subgroup, a form of social mitosis. A second simulation demonstrates that a starting configuration containing only positive and negative relations (no null relations) will completely determine who will eventually belong to which of the two groups. As null relations become more plentiful in the starting configuration, the order or historical trajectory of restoring balance among triads also contributes to subgroup membership.

The Competition for Attention and the Evolution of Science

Warren Thorngate, Jing Liu and Wahida Chowdhury
Journal of Artificial Societies and Social Simulation 14 (4) 17

Abstract: Whenever the amount of information produced exceeds the amount of attention available to consume it, a competition for attention is born. The competition is increasingly fierce in science where the exponential growth of information has forced its producers, consumers and gatekeepers to become increasingly selective in what they attend to and what they ignore. Paradoxically, as the criteria of selection among authors, editors and readers of scientific journal articles co-evolve, they show signs of becoming increasingly unscientific. The present article suggests how the paradox can be addressed with computer simulation, and what its implications for the future of science might be.

Measuring Simulation-Observation Fit: An Introduction to Ordinal Pattern Analysis

Warren Thorngate and Bruce Edmonds
Journal of Artificial Societies and Social Simulation 16 (2) 4

Abstract: Most traditional strategies of assessing the fit between a simulation's set of predictions (outputs) and a set of relevant observations rely either on visual inspection or squared distances among averages. Here we introduce an alternative goodness-of-fit strategy, Ordinal Pattern Analysis (OPA) that will (we argue) be more appropriate for judging the goodness-of-fit of simulations in many situations. OPA is based on matches and mismatches among the ordinal properties of predictions and observations. It does not require predictions or observations to meet the requirements of interval or ratio measurement scales. In addition, OPA provides a means to assess prediction-observation fits case-by-case prior to aggregation, and to map domains of validity of competing simulations. We provide examples to illustrate how OPA can be employed to assess the ordinal fit and domains of validity of simulations of share prices, crime rates, and happiness ratings. We also provide a computer programme for assisting in the calculation of OPA indices.