Life is an Adventure! An agent-based reconciliation of narrative and scientific worldviews

scientific modeling paradigm. This newfound flexibility is part of what has made the
MAS and CAS perspectives so popular in such a relatively short time: by generalizing
the already successful notion of dynamic system following a (deterministic or
stochastic) trajectory to the more flexible notion of agent(s) following a goal-directed
course of action it has opened the way to unifying physical, biological and social
sciences [Holland, 1992; Miller et al., 2007].

My contention here is that not much is needed to also include the humanities,
with their focus on literature, history and religion, into that emerging unification. The
agent-based approaches have as yet not explored the interactions between prospect
and mystery: most agents in existing computer simulations have a fixed (and typically
very limited) prospect. They can only plan one or two steps ahead. Therefore, they
cannot anticipate increasing their prospect, which I defined as the experience of
mystery. The only mystery exists for the observer who is running the simulation, and
who is curious to see what may emerge from it. In that sense, typical agents in the
CAS/MAS tradition are rather shortsighted, and would not fit into the role of a
mythological hero. What is needed to recover the full subtlety and complexity of
human life is a prospect that can be stretched or shrunk depending on the context, so
that distant ideals can define a long-term quest, while unplanned diversions create
more short-term challenges.

An elegant example of such a more realistic agent-based simulation model is
the “virtual laboratory” that was designed by Gershenson [2004; Gershenson et al.,
2002] to experiment with different cognitive strategies for behavior. Gershenson’s
agents navigate through a virtual, three-dimensional environment in search of
affordances, such as food and water, while trying to avoid disturbances, such as rocks
and predators. These diversions are generated by the program at random times and
positions across the environment. Their appearance thus constitutes a true,
unforeseeable “surprise”. However, the agents have a limited prospect or field of
vision (similar to the one in Fig. 3), allowing them to perceive all diversions within a
certain radius and angle that are not hidden behind obstacles [Gershenson et al.,
2002]. Assuming that a diversion rarely appears in their immediate vicinity, this
means that they have in general time to adapt their course of action in reaction to the
diversions that appear in their prospect. Thus, they can start fleeing in the opposite
direction as soon as they perceive a predator ahead, or change course from a more
remote or smaller source of food to a more nearby or larger one that has just entered
their field of vision.

The agent’s course is visualized as a trail left behind by the agent’s
movements across the virtual space. This makes it possible to examine a course of
action both in “narrative mode” as a real-time succession of movements, and in
“scientific mode” as a fixed trajectory. The virtual laboratory synthesizes narrative
and scientific perspectives in other respects too: a single “run” of the simulation can
be seen as a virtual adventure, idiographically describing the things happening to a
specific agent in a specific context. However, when a large number of such unique
runs have been generated (differing in the values of random diversions or the initial

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