DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

(τ = Time delay and σ : Standard deviation of ρ )
The global performance of the ecosystems can be obtained from the above equation. Under
different conditions of delay, uncertainty, cooperation/competition the system shows a rich
panoply of behaviors ranging from stable, sustained oscillations to intermittent chaos and finally
to fully developed chaos. Furthermore, following generic deductions can be made from this
model (Kephart et al. 1989): While information delay has adverse impact on the system
performance, uncertainty has a profound effect on the stability of the system. One can
deliberately increase uncertainty in agents’ evaluation of the merits of choices to make it stable
but at the expense of performance degradation. Second possibility is very slow reevaluation rate
of the agents, which however makes them non-adaptive. Heterogeneity in the nature of agents
can however lead to more stability in the system compared to homogenous case but the system
loses its ability to cope up with unexpected changes in the system such as new task requirements.
On the other hand poor performance can be traced to the fact that the non-predictive agents do not
take into account the information delay.
If the agents are able to make accurate predictions of its current state, the information delay
could be overcome, and the system would perform well. This results in a “co-evolutionary”
system in which all of the individual are simultaneously trying to adapt to one another. In such a
situation agents can act like Technical Analysts and System Analysts (Kephart et al. 1990). Agents
as technical analysts (like those in market behavior) use either linear extrapolation or cyclic trend
analysis to estimate the current state of the system. On the other hand, agents as system analysts
have knowledge about both the individual characteristics of the other agents in the system and
how those characteristics are related to the overall system dynamics. Technical Analysts are
responsive to the behavior of the system, but suffer from an inability to take into account the
strategies of other agents. Moreover good predictive strategy for a single agent may be disastrous
if applied on a global scale. System Analysts perform extremely well when they have very
accurate information about other agents in the system, but can perform very poorly when their
information is even slightly inaccurate. They take into account the strategies of other agents, but
pay no heed to the actual behavior of the system. This suggests combining the strengths of both
methods to form a hybrid- adaptive system analyst-, which modifies its assumptions about other
to feedback about success of its own predictions. The resultant hybrid is able
agents in response
to perform well.
In order to avoid chaos while maintaining high performance and adaptability to unforeseen
changes more sophisticated techniques are required. One such way is by reward mechanism
(Hogg and Huberman 1991) whereby the relative number of computational agents following
effective strategies is increased at the expense of the others. This procedure, which generates a
right mix of diverse population out of essentially homogenous ones, is able to control chaos by a
series of bifurcations into a stable fixed point.
In the above description each agent chooses amongst different resources according to its
perceived payoff, which depends on the number of agents already using it. Even the agent with
predictive ability is myopic in its view, as it considers only its current estimate of the system
state, without regard to the future. Expectations come into play if agents use past and present
global behavior in estimating the expected future payoff for each resource. A dynamical model of
collective action that includes expectations can be found in (Glance 1993).
6. Models from Observed Data
One of the central problems in a supply chain, closely related to modeling, is that of demand
forecasting: given the past, how can we predict the future demand? The classic approach to
forecasting is to build an explanatory model from the first principle and measure the initial
conditions. Unfortunately this has not been possible for two reasons in systems like supply
chains: Firstly, we still lack the general “first principles” for demand variation in supply chains,