Automated extraction of movement rationales for building ABMs: Sengupta et al. (2018)

I found both of these articles really fascinating, and found it helpful to understand the theories and differences between the ALGAs and SGAs presented in Sengupta and Sieber (2007), especially in analyzing the ABM presented in Sengupta et al. (2018).

Sengupta et al. (2018) use field-recorded data on Red Colobus monkey location and movements in space and time, combined with other GIS data (land cover type, slope) to automate ABM movement rules in an ALGA. Sengupta and Sieber (2007) suggest that ALGAs began with studying the flocking behaviours of animals and birds. To me, this suggests that the effects of a study on animals can have broad-reaching effects, beyond the study of “movement ecology” (2).

Sengupta et al. (2018) suggest that the advancement of high-resolution tracking technologies have created an ‘“enormous volume” (2) of data (i.e. big data). In this study, the authors refer to big data derived from GPS tags on animals, but couldn’t this easily be expanded to the movement-tracking (big data-creating) devices we carry around with us all day? Is there justification for concern given the necessarily reductive and therefore inherently wrong nature of models? Are our movements as easy to predict as a Red Colobus Monkeys’?

Though I tend to be a bit a doomsday-ist and cynic, in this case, I think that though models may try to track behaviours and predict movements, both humans and animals have one things the models don’t have: free will. Though the models can incorporate and automate complex decision-making models, I think that humans and monkeys and various other animals sometimes make irrational decisions that models cannot predict. In fact, I think that there is a huge potential for error in these models, which neither article addresses.

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