Showing papers by "Gary Polhill published in 2023"
TL;DR: In this paper , the authors present the range of positions on agent-based models and prediction, tackling methodological, epistemological and pragmatic issues, concluding that many other purposes to which it might be applied are more worthy of consideration than prediction, including explanation, improving data collection, testing theories and suggesting analogies.
Abstract: ABSTRACT Agent-based models (ABMs) have their origins in considerations of complexity science stipulating that many phenomena can be ‘grown from the bottom up’. Explicitly, this was expressed in Epstein & Axtell’s (1996) Growing Artificial Societies as the change from ‘Can you explain it?’ to ‘Can you grow it?’. In 2008, Epstein published an article entitled Why Model? in which he discussed his exasperation with people asking for predictions from ABM, pointing out that many other purposes to which it might be applied are more worthy of consideration than prediction, including explanation, improving data collection, testing theories and suggesting analogies. Fourteen years later, the debate about the predictive powers of ABM is still unresolved. This special issue presents the range of positions on ABM and prediction, tackling methodological, epistemological and pragmatic issues.
TL;DR: In this article , the authors compare the characteristics of natural language-based and simulation-based decision-making, and argue that computational tools for decision making can and should be complementary to natural language discourse approaches, but this requires that both systems are used with their limitations in mind.