Abstract
This article proposes a direct relationship between complexity and predictability in a two-agent noncooperative zero-sum game (2XZSG). The author explores this proposition by modeling armed conflict as a 2XZSG and using case studies in armed conflict as the dataset for the systematic literature review. This article uses a multiple case study approach, systematically reviewing 13 case studies in armed conflict that yielded 156 references identifying four themes—environmental, human resource, operational, and supply chain constraints—that demonstrate a direct relationship between complexity and predictability. The data focuses on decisions made in particular battles and campaigns as well as the constraints that impacted decision making. By identifying those decisions and constraints, four themes emerged. These four themes are an innovation as a potential addendum to the war gaming methodology in the military decision making process (MDMP).
Highlights
Concepts DefinedThe systematic literature review (SLR) is a well-established method of inquiry for social science research and is defined as an essential component of academic research.[1]
McCullin has worked as an infrastructure analyst at the U.S Department of Homeland Security, and prior to that retired from the U.S Army with various tours, serving 12 years in Army Special Operations with the U.S Army Civil Affairs and Psychological Operations
The theoretical lens for this study draws from the constructs of four established theories: game theory, complex systems theory, bounded rationality, and the theory of constraints
Summary
The systematic literature review (SLR) is a well-established method of inquiry for social science research and is defined as an essential component of academic research.[1]. These characteristics define armed conflict as a 2XZSG In this context, combatants are players who initiate combat operations (i.e., rounds of the game where actions produce an end state) that result in victory (i.e., maximizing payoff with minimum cost) or defeat (i.e., maximizing cost with minimum payoff). Goldratt explains that the theory of constraints is the basis for defining change as continuously improving performance He argues that constraints limit system performance.[9] Mahesh Gupta and Joseph Kline identify and manage constraints with a five-step process: 1) find the system limits; 2) decide how to make full use of the system limits; 3) offer full support to those decisions; 4) break the system limits; and 5) continue to identify new constraints.[10] These are the constructs employed in this article. This article uses an SLR to examine the relationship between complexity and predictability in zero-sum games using a multiple case study approach The model for this exploration is based on armed conflict
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