Thoughts on ‘ Geospatial Agents, Agents Everywhere…’ (Sengupta, Sieber 2007)

I liked how this review delineated very specifically the difference between Artificial Life Agents and Software Agents because I was quite ignorant about the latter before.

What struck me about the four conditions necessary for classification as an intelligent agent, each criteria is the result of many different interacting subfields. For example, the possession of rational behaviour is dependent on decision-making theory from psychology, economics, reward learning paradigms, and machine learning algorithms. These varied fields all have something to contribute to the “rational behaviour” required of an intelligent agent. This suggests that agents are the product of interdisciplinary sciences, and supports the findings that they have very diverse applications.

The major distinction I made between ALGAs and Software Agents was that ALGAs are concerned with intra-human relations; behaviour, interactions between social beings, and the learning that arises from these interactions, whereas Software Agents are concerned with making inter-human processes easier, namely the retrieval and manipulation of spatial data though a computer interface. I am not sure if this distinction captures the true differences between the two types of agents. Are software agents really so removed from the inner workings of humans? Do they not need an awareness of the user and the user’s capabilities, limitations, responses to the environment in order to facilitate easier task processing?

The authors discuss how initial forays into AI research were met with “disillusionment with their true potential in mirroring human intelligence”. This doesn’t surprise me because I think as a society we tend to idealize new technologies when they arise, and overestimate their explanatory, predictive, or transformative power. An example is the use of fMRI and the huge spike in using neural data to explain every conceivable phenomena. AI is also having a major moment, with its use in categorizing human faces for facial recognition to its use in perfecting self-driving cars.  I wonder if agent based modelling, promising though it is in terms of testing out predictions and tweaking specific variables to see the outcomes, is also one of those overly hyped technologies? Is it intrinsically better than naturalistic observation in coming to conclusions about behaviour, or are they different tools to answer different kinds of questions?

The discussion about ALGAs immediately reminded me of agent-based modelling by Thomas Schultz in McGill’s psychology department that modelled different cooperation strategies (ethnocentric, allocentric) to see which one was most evolutionarily successful. This makes me think about how much we can extrapolate from geospatial agent based modelling, especially when dealing with a very granular issue like the behaviour of individual organisms. Can we make widespread predictions about the real world based on these simulations?

The discussion about the various applications of ALGAs, from migratory behaviour to models of urban sprawl, raises questions about what kinds of problems ALGAs are more suited to, and what metric could be used to determine the appropriateness of agent-based modelling for a given issue. This might help curtail the tendency to use the technology for every kind of problem (even those which might benefit from another approach.) Developing some way of testing the adequacy of the method for the issue would also help focus the predictive power and efficacy of the generated models. This is a framework that could be built upon a comprehensive review of the different kinds of issues which geospatial agent based modelling is being used to solve.




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