Computational Emergence and Computational Emotion
D.N.Davis
IEEE International Symposium on Systems, Man and Cybernetics
Tokyo, Japan, 1999
Abstract
This paper presents an analysis of emergence and emotion in computational
(cognitive) systems. Computational emergence can be categorised as one
of four main types: diachronic emergence which describes computational
states that emerge over time, typically measured in terms of evolutionary
periods; gestalt emergence describes the development of multiple process
models related to the different levels of abstraction that can be placed
on a computational system; representational emergence where behaviour at
least one layer in a multi-layer computational system emerges as representational
schemes at other layers; and adaptive emergence whereby beneficial capabilities
emerge through design of supporting communication and feedback mechanisms.
The first category describes, for example, the benefits from using evolutionary
algorithms to design problems; the resulting optimized designs are a diachronic
result emerging from the use of such systems. Related to these patterns
of emergence, are different types of sub-cognitive bias. For example, an
assumption that collective (and co-operative) activity will emerge opportunistically.
A consideration of individualistic bias suggests that adaptive emergence
needs both feedback mechanisms from the micro (individual) level to the
macro (societal) level and from the societal level to the individual level
(a kind of focus of attention in a macro-agent). Irrespective of the tasks
modeled and whether internal to a complex system or the human interaction
with such systems, perturbant computational behaviour analogous to human
emotive states can occur. This type of problematic emergent behaviour does
not easily fit with the categories already introduced. The remainder of
this paper considers how a developing computational theory of cognition
can be used to monitor and manage such emergent states before the computational
system becomes dysfunctional. The theory maps onto a distributed multi-layer
computational model which makes use of tightly and loosely coupled agents
working within a holistic system.