To us, ABM is primarily a concern with representation, and though some environments and languages are more convenient than others, it matters little how that is implemented in a computer program.
There is intersection, but not equivalence, between ABM and software agents. 159) that “there is no generally agreed definition of what an ‘agent’ is.” Some of the controversy has its origins in confusing representation with implementation, with software agents sometimes being used to implement ABMs. Writing in 1999, Gilbert and Troitzsch observe (p. Controversies over whether the entities in a model qualify as ‘agents’, for example, have a long history. One difficulty with ABM is defining what it is authoritatively.
With all these advantages, it is strange that ABM is not routinely taught in undergraduate courses (particularly in geography, psychology, sociology, economics, politics) and policymakers, businesses, banks and research funders are not typically expecting analyses to involve ABM. With ABMs dating back at least to the 1960s (such as Schelling and Sakoda both modelling spatial dynamics of homophily ), it can hardly be said to be especially new. It has been applied to a wide range of case studies from water use to traffic simulation, and can be applied to modelling proteins as agents to modelling interactions among nation states. It can explore formalizations of theories, and is less prone to hiding assumptions, making it easier to use with stakeholders. It can capture complex dynamics that are infeasible, inelegant, or oversimplified when addressed in other modelling approaches. It can be spatially explicit and simulate social interactions under biophysical constraints. ABMs can simulate heterogeneous interacting individuals making decisions about their behaviour. The case for agent-based modelling (ABM) has been made by several authors. This leads us to identify several areas where work is needed. Moore’s Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises.
Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. Partly, this is an issue of awareness – ABM is still new enough that many people have not heard of it partly, it is an issue of confidence – ABM has more to do to prove itself if it is to become a preferred method. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge.