Different Modelling Purposes

Abstract

How one builds, checks, validates and interprets a model depends on its 'purpose'. This is true even if the same model code is used for di erent purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be rechecked, re-validated and maybe even rebuilt in a di erent way. Here we review some of the di erent purposes for a simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these 'dangers' might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

Mutual aid: When does resource scarcity favour group cooperation?

“…The resourcebased approach of inter-individual interactions allows this model to go further than a game-theory approach on the impacts of agents on the resource and on the dynamics of this resource. However, this model was developed for illustration purpose (Edmonds et al, 2019) and do not claim to make any prediction on the real world.…”

Individual attitude change and societal dynamics: Computational experiments with psychological theories

“…It is out of the scope of this article to provide a detailed discussion of the epistemology of simulation (Morgan & Morrison, 1999;Humphreys, 2004;DeLanda, 2015) and different purposes of social simulation in particular (Edmonds, 2017). However, we specify the use of the particular modelling approach utilized here and its potential for social psychology.…”

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

“…Applying ABM in the empirical world means facing up to prediction and validation. Edmonds [137] outlines five purposes for modelling: prediction, explanation, theoretical exposition, description and illustration. As he defines them, prediction is reliably anticipating unknown data with useful accuracy (p. 42), explanation is about identifying plausible causal chains (p. 45), theoretical exposition concerns the establishment of general principles (p. 48) that need not necessarily apply in the real world (p. 49), description entails the (partial) representation of a real-world situation (p. 50), and illustration is, in essence, using ABM for the purposes of visualization (p. 53).…”