Limited Rationality in Action:
Decision Support for Military Situation Assessment
Kathryn Blackmond Laskey
Bruce D'Ambrosio
Tod S. Levitt
Suzanne Mahoney
Abstract
Information is a force
multiplier. Knowledge of the
enemyıs capability and intentions may be of far more value to a military force
than additional troops or firepower. Situation assessment is the ongoing
process of inferring relevant information about the forces of concern in a
military situation. Relevant
information can include force types, firepower, location, and past, present and
future course of action. Situation
assessment involves the incorporation of uncertain evidence from diverse
sources. These include
photographs, radar scans, and other forms of image intelligence, or IMINT;
electronics intelligence, or ELINT, derived from characteristics (e.g.,
wavelength) of emissions generated by enemy equipment; communications
intelligence, or COMINT, derived from the characteristics of messages sent by
the enemy; and reports from human informants (HUMINT). These sources must be combined to form
a model of the situation. The sheer volume of data, the ubiquity of
uncertainty, the number and complexity of hypotheses to consider, the
high-stakes environment, the compressed time frame, and deception and damage
from hostile forces, combine to present a staggeringly complex problem. Even if one could formulate a decision
problem in reasonable time, explicit determination of an optimal decision policy
exceeds any reasonable computational resources. While it is tempting to drop any attempt at rational
analysis and rely purely on simple heuristics, we argue that this can lead to
catastrophic outcomes. We present
an architecture for a "complex decision machine" that performs
rational deliberation to make decisions in real time. We argue that resource
limits require such an architecture to be grounded in simple heuristic reactive
processes. We thus argue that both simple heuristics and complex decision machines are
required for effective decision making in real time for complex problems. We describe an implementation of our
architecture applied to the problem of military situation assessment.
KEY WORDS: Belief networks, knowledge representation, agile modeling, bounded rationality, decision theory