Abstract
Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.
Highlights
Movement studies have recently developed Artificial Intelligence (AI) approaches to implement ‘thinking machines’ for modelling movement behaviours [13,14,15,16,17]
Agent-Based Models (ABM) is founded on the idea that the real world can be computationally represented by a collection of adaptive decision-makers and a set of rules governing their interactions within an environment [23]
One data-driven and one simulation-based school of thought (GCM and ABM in practice) both claim to explore scientific causally relevant evidence. Both approaches are founded on the idea that an initial model of a phenomenon is necessary for causal analysis
Summary
Movement studies have recently developed Artificial Intelligence (AI) approaches to implement ‘thinking machines’ for modelling movement behaviours [13,14,15,16,17]. For example, is a popular form of AI that has been successful in making associations to recognize patterns and generate stochastic models, but without producing explicit explanations. Through fitting a function to the data, with no requirement or production of reasoning-models [18], deep neural networks reduce causality to an ad-hoc question of predictability. The ‘model-based’ approaches envisioned by the pioneers of AI, in contrast, involve an explicit representation of knowledge and reasoning [19]. In the context of GIScience, some scholars have been seeking a solution through the application of agent-based models (ABM) [20,21,22], in which the modellers’ prior knowledge of phenomena is encoded to expand on. There has been serious criticism against ABM as a relevant method for causal inference due, for example, to the simplistic assumption that causally-relevant evidence can be inferred from common-sense simulation models such as ABM
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