Cost-efficiency analysis of weapon system portfolios

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SIMULATING WAR: Studying Conflict through Simulation Games

The problem of designing an effective future defense force is quite complex and challenging. One methodology that is often employed in this domain is portfolio optimization, whereby the objective is to select a diverse set of assets that maximize the return on investment. In the defense context, the return on investment is often measured in terms of the capabilities that the investments will provide. While the field of portfolio optimization is well established, applications in the defense sector pose unique challenges not seen in other application domains. However, the literature regarding portfolio optimization for defense applications is rather sparse. To this end, this paper provides a structured review of recent applications and identifies a number of areas that warrant further investigation.

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Portfolio Optimization for Defence Applications

Advances in Modeling and Simulation

Big data technology has undergone rapid development and attained great success in the business field. Military simulation (MS) is another application domain producing massive datasets created by high-resolution models and large-scale simulations. It is used to study complicated problems such as weapon systems acquisition, combat analysis, and military training. This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then we looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Two frameworks were also surveyed to trace the development of the underlying software platform. Finally, we identified some key challenges and proposed a framework as a basis for future work. This framework considered both the simulation and big data management at the same time based on layered and service oriented architectures. The objective of this review is to help interested researchers learn the key points of MS big data and provide references for tackling the big data problem and performing further research.

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Military Simulation Big Data: Background, State of the Art, and Challenges

: Data Farming is a process that has been developed to support decision-makers by answering questions that are not currently addressed. Data farming uses an inter-disciplinary approach that includes modelling and simulation, high performance computing, and statistical analysis to examine questions of interest with large number of alternatives. Data farming allows for the examination of uncertain events with numerous possible outcomes and provides the capability of executing enough experiments so that both overall and unexpected results may be captured and examined for insights. In 2010, the NATO Research and Technology Organization started the three-year Modeling and Simulation Task Group Data Farming in Support of NATO to assess and document the data farming methodology to be used for decision support. This final report documents the completed work of this Task Group, designated MSG-088. It includes a description of the six realms of data farming as well as the two case studies performed during the course of MSG-088. This report leverages the knowledge of data farming experts to codify data farming, describing how best to perform data farming to gain insight into questions in support of decision makers.

Data Farming in Support of NATO

Field experiments were used to evaluate three different indirect fire models: the cookie cutter and the Carleton damage functions and a simplified physical model for fragmenting ammunition. Data fr...

Validating indirect fire models with field experiments

We used Sandis simulation tool to study a historical battle of Loimola in summer 1944. The first goal was to study war history and address a novel method in Finnish environment. The second goal is to publish the methods used in military analysis. Instead of the classified questions and scenarios in modern Finnish defense, the battle of Loimola 27th July 1944 was used to get a public example of the analysis method. As a third goal, this simulation is an example of the tens of historical simulation studies needed to validate simulation software. 
Sandis simulation tool has been used for cost-effect and tactical analysis in Finnish defense forces. With Sandis the human operator is responsible for tactical decisions during the scenario building phase. During the calculation phase the computer calculates the combat losses, platoon/squad states (operational, defeated or destroyed), ammunition consumption and also other values not used in this study. During the analysis phase, charts, tables and killer-victim scoreboards are created.
Our case study focuses on the day of 27th July 1944, when the Soviets assaulted the Finnish defense lines near Lake Suovajarvi. The unit hierarchies, weapons, weapon parameters, positions and movements of the units were set as input. The results match the historical events: the Finnish losses were 30 according to the literature; simulated loss distribution (95%) was between 25 and 44, mean 35. In the simulations the attacking units are defeated and stopped as in literature. The ratio of losses caused by artillery and light infantry weapons did not conflict the values from larger area available from the literature. Thus in this case study, Sandis performed well. 
In future work, different historical battles could be simulated in order to get better insight of our history and key issues of war, helping to improve our future defense.

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COMPARING SIMULATED RESULTS AND ACTUAL BATTLE EVENTS FROM 1944 – A case study using Sandis software

We develop a dynamic Markovian method of simulating a battle between two infantry units. Its key feature is that the probabilities of the outcomes of the battle can be computed efficiently, without the joint distribution of the strengths of the units or their transition matrix, making the method feasible even with larger unit strengths. We find the probabilities of the outcomes to be close to the ones obtained from a more elaborate, but computationally more costly, joint Markov-chain model of strengths. Additionally, using our method we are able to compute the conditional distributions of the strength of a unit, given that it has, respectively, won the battle or been defeated by the enemy.

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An approximative method of simulating a duel

Technology forecasting serves two purposes in the military context: planning investments of future weapon systems and anticipation of an adversary’s future capabilities. In the former, different weapon systems that will be available in the future are modelled and their contribution to one’s own performance is analyzed. This analysis supports the decision making regarding what kind of investments should be made to optimize our future performance. In the latter, the adversary’s possible future weapon systems are modelled and analyzed. This gives suggestions about preventative actions for meeting the future challenges. To simulate the course of a battle on operational level, autonomous simulation software is usually insufficient. Instead, one needs to employ wargaming in which tactical decisions are made by a human operator and weapon system effects are simulated by the software, so called man-inthe-loop simulation. In this paper we focus on one such software which enables wargaming: combat modelling tool Sandis [1], which is developed at the Finnish Defence Forces Technical Research Centre. Sandis is based on probability calculus and fault logic analysis and can be used for comparative scenario-based analysis from platoon to brigade level.

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Esa Lappi

Papers

Data Farming in Support of NATO

Validating indirect fire models with field experiments

COMPARING SIMULATED RESULTS AND ACTUAL BATTLE EVENTS FROM 1944 – A case study using Sandis software

An approximative method of simulating a duel

Warfare Simulation and Technology Forecasting in Support of Military Decision Making