Emergent behavior identification within an agent-based model of the Ballistic Missile Defense System using statistical debugging

Abstract

In this paper, we utilize a software engineering technique called statistical debugging to experimentally identify emergent behaviors in a System of Systems. We apply the technique to an agent-based model of the Ballistic Missile Defense System (BMDS) in which each component of the BMDS has operational and managerial independence as well as geographic distribution. We develop a simple game where the BMDS (blue) is attacked by enemy threats (red) and repeat the game for a large number of replications. Results show that there is no strategy that guarantees a blue win. In addition, there are more losing strategies than winning ones and finally there are four strategies that appear in half the wins and the losses. We refer to those as emergent, since they cannot be controlled by either the red or the blue side.

Architecting systems of systems: A tertiary study

“…that distinguish an SoS from other systems: the operational and managerial independence of the constituents. Based on these characteristics, this concept has been applied in complex systems from a wide variety of domains, including defense [2,3], manufacturing [4,5], emergency systems [6,7], energy [8,9], and health care [10,11]. The main motivation to use a SoS approach in all these domains, is having high-level goals that cannot be tackled separately by the constituents, but could eventually be fulfilled by their synergy.…”

Detecting emergence in engineered systems: A literature review and synthesis approach

“…We want to identify and take advantage of the positive emergence, while detect and mitigate negative emergence. Many methods have shown promising results in different case studies 1–3,17,28,30,36,49–54,56–67,70,71,73–80 . The approaches range from intuitive specialized model views used by Guariniello et al 1 .…”

A content analysis-based approach to explore simulation verification and identify its current challenges

“…Many other forms of LHS designs exist that differ in how the sample combinations are generated and how many samples are generated, such as orthogonal and nearly orthogonal designs [99]. Diallo, Lynch [111] and Collins, Seiler [104] both relied on LHS within their experimental designs to reduce the total number of runs needed and save time generating data. SA analyzes how uncertainty in the model output can be attributed to uncertainty in the model input [112].…”

“…Statistical debugging using elastic predicates and many-valued labeling functions has been developed for the exploration of simulation using software engineering principles and directly accounts for sample sizes when making verification determinations [82]. Statistical debugging delves into complex simulation interactions without requiring a formal mathematical model specification to identify and isolate locations of potential errors [61,82,111]. Diallo, Lynch [111] apply statistical debugging to sets of thousands of simulation outcomes pulled from an experimental design producing thousands of simulation runs per batch.…”

“…Statistical debugging delves into complex simulation interactions without requiring a formal mathematical model specification to identify and isolate locations of potential errors [61,82,111]. Diallo, Lynch [111] apply statistical debugging to sets of thousands of simulation outcomes pulled from an experimental design producing thousands of simulation runs per batch. Utilizing the V&V Calculator [81], adherence to dozens of model specifications were explored in each test and no violations were identified within the final testing set.…”