A research study of human decision-making in higher level military command and control

In a paper by Huber (1985), based on the findings of a symposium on Modelling and Analysis of Defence Processes, 1982, once recommendation made is that “major research efforts are required with regard to the human decision-making processes in command and control s approach based on experiments in a realistic decision environment should be pursued.”. This paper reports on two years of such effort, part of a larger programme of study of Naval C³I (Command, Control, Communications and Intelligence). It focuses on the Commander of a Task Force, the nature of his role, his requirements for information, and his need to be able to reflect on his options and their possible military and political consequences. Structured interviews, games and psychological tests are integrated in a continuing experimental programme. Analysis includes the use of cognitive mapping and content analysis. The aim is to determine the sorts of decision aids which might be provided and the use of such aids for training and learning.     

Validation of the mission-based approach to representing command and control in simulation models of conflict

There is a need to represent military command and control in closed-form simulation models of conflict, in order to compare investment in such capability with alternative defence investments. This paper considers such representation of military command and control in the context of embodied cognitive science. This means that we represent such processes in terms of both decision-making and resultant behaviour. Previous work leads to the view that such a representation can be captured by a combination of deliberate (top down) planning and rapid (bottom up) planning. We have developed an approach on these lines as a way of representing human decision-making and behaviour in conflict. Here we show, by comparing simulation model results with real conflict situations, that our approach yields emergent force behaviour which is valid and representative. This thus increases our confidence that our representation of command and control in such simulation models is sufficient for our requirements.     

Devolving command decisions in complex operations

In contemporary military endeavours, Command and Control (C2) arrangements generally aim to ensure an appropriate regulation of command-decision autonomy such that decision makers are able to act in a way that is consistent with the overall set of commanders’ intents and according to the nature of the unfolding situation. This can be a challenge, especially in situations with increasing degrees of uncertainty, ambiguity and complexity, also where individual commanders are faced with conflicting objectives. Increasingly, it seems that command decisions are being taken under conditions of internal command contention; for example, when the likely successful outcome of a tactical mission can often be at odds with the overall strategic and political aims of the campaign. The work in the paper builds on our previous research in decision making under uncertainty and conflicting objectives, where we analysed the responses of military commanders in decision experiments. We demonstrated how multi-attribute utility theory could be used to represent and understand the effects of uncertainty and conflicting objectives on a particular commander's choices. In this paper, we further develop and generalize the theory to show that the geometrical forms of expected utilities, which arise from the assumption of commander rationality, are qualitatively stable in a wide range of scenarios. This opens out into further analysis linking to Catastrophe Theory as it relates to C2 regulatory frameworks for devolving command decision freedoms. We demonstrate how an appreciation of this geometry can aid understanding of the relationship between socially complex operational environments and the prevailing C2, which can also inform selection and training of personnel, to address issues of devolving command decision-rights, as appropriate for the endeavour as a whole. The theory presented in the paper, therefore, provides a means to explore and gain insight into different approaches to regulation of C2 decision making aimed ultimately at achieving C2 agility, or at least at a conceptual language to allow its formal representation. C2 regulatory agents are discussed in terms of detailed functions for moderating command decision making, as appropriate for the degrees of uncertainty and goal contention being faced. The work also begins to address implications of any lack of experience and any differences in personality-type of the individual commanders with respect to risk-taking, open-mindedness and creativity.     

The NATO strategic commanders' assessment of requirements for strategic sea and airlift

With the end of the Cold War and the adoption of a new NATO strategic concept, NATO nations now emphasize the use of mobile, flexible, and multinational military forces to be deployed for a myriad of crisis response operations outside the normal NATO treaty area. One critical shortfall for a number of NATO members has been the ready availability of strategic sea and airlift assets. Most NATO nations rely on the civilian transport market to provide dry cargo vessels and wide-body cargo aircraft. The two NATO strategic commands, Supreme Headquarters Allied Powers Europe (SHAPE) and Headquarter Supreme Allied Command Atlantic (SACLANT), have been assessing the requirements for sea and airlift and have proposed to nations a variety of measures to improve their movement and transport capability. SACLANT staff has been developing the operations research methods to assist in this assessment. The current paper conveys the methodology and the mathematical programming model used in this analysis, using ship, aircraft, and port availability constraints. Furthermore, the paper presents a framework of the modeling and simulation capability of the NATO commands and agencies in the movement and transportation area. Additionally, the paper provides an example of the use of simulation models in follow-up activities such as high-level seminars in which national military force planners and movement and transport staff participate. During these events, participants are provided with the results from a number of transportation simulation models, in an attempt to steer the decision making process in NATO.     

Continuous management of airlift and tanker resources: A constraint-based approach

Efficient allocation of aircraft and aircrews to transportation missions is an important priority at the USAF Air Mobility Command (AMC), where airlift demand must increasingly be met with less capacity and at lower cost. In addition to presenting a formidable optimization problem, the AMC resource management problem is complicated by the fact that it is situated in a continuously executing environment. Mission requests are received (and must be acted upon) incrementally, and, once allocation decisions have been communicated to the executing agents, subsequent opportunities for optimizing resource usage must be balanced against the cost of solution change. In this paper, we describe the technical approach taken to this problem in the AMC barrel allocator, a scheduling tool developed to address this problem and provide support for day-to-day allocation and management of AMC resources. The system utilizes incremental and configurable constraint-based search procedures to provide a range of automated and semi-automated scheduling capabilities. Most basically, the system provides an efficient solution to the fleet scheduling problem. More importantly to continuous operations, it also provides techniques for selectively reoptimizing to accommodate higher priority missions while minimizing disruption to most previously scheduled missions, and for selectively “merging” previously planned missions to minimize nonproductive flying time. In situations where all mission requirements cannot be met, the system can generate and compare alternative constraint relaxation options. The barrel allocator technology is currently transitioning into operational use within AMC's Tanker/Airlift Control Center (TACC). A version of the barrel allocator supporting airlift allocation was first incorporated as an experimental module of the AMC's Consolidated Air Mobility Planning System (CAMPS) in September 2000. In May 2003, a new tanker allocation module is scheduled for initial operational release to users as part of CAMPS Release 5.4.     

Markovian representation of a bilinear time series model and maximum likelihood estimation of the parameters

A bilinear time series (BLTS) model is expressed in the form of Akaike's Markovian representation in order to use the Kalman recursive estimation approach. It is shown that Akaike's Markovian representation of autoregressive moving average models of orderp and q (ARMA(p,q)) and that of the bilinear model are equivalent. This equivalence facilitates the maximum likelihood estimation of the parameters involved in the bilinear model, which otherwise is an unwieldy problem. The present approach can easily be extended to take into account missing observations     

Bayesian process optimization using failure amplification method

This work is motivated by a problem of optimizing printed circuit board manufacturing using design of experiments. The data are binary, which poses challenges in model fitting and optimization. We use the idea of failure amplification method to increase the information supplied by the data and then use a Bayesian approach for model fitting. The Bayesian approach is implemented using Gaussian process models on a latent variable representation. It is demonstrated that the failure amplification method coupled with a Bayesian approach is highly suitable for optimizing a process with binary data. Copyright © 2010 John Wiley & Sons, Ltd.     

Solution of a Differential Game Formulation of Military Air Operations by the Method of Characteristics

In this paper, we describe a zero-sum differential game formulation for the control of military air operations. The model consists of a system of nonlinear ordinary differential equations for the dynamics of the operations and a suitably chosen quadratic payoff function. The control variables are the engagement intensities and velocities, and there are constraints on the controls. The method of characteristics (based on the Pontryagin maximum principle) is used to solve the associated Hamilton-Jacobi equation. In this nonlinear formulation, the Hamiltonian can be optimized explicity with respect to the controls. Numerical simulations study the enforcement of constraints (a) by means of penalties in the payoff function or (b) explicitly. The numerical results show robustness with respect to various parameters.Effort sponsored by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory, Air Force Material Command, USAF, under Agreement F30602-99-2-0551. A shorter version of this paper appeared in the Proceedings of the 2001 American Control Conference, pp. 168--175, 2001.     

Bayesian decision making and military command and control

We discuss firstly the problem of military decision, in the context of the more general development of ideas in the representation of decision making. Within this context, we have considered a mathematical model—Bayesian Decision—of decision making and military command. Previous work has been extended, and applied to this problem. A distribution of belief in outcome, given that a decision is made, and a Loss function—a measure of the effect of this outcome relative to a goal—are formed. The Bayes' Decision is the decision which globally minimises the resultant bimodal (or worse) Expected Loss function. The set of all minimising decisions corresponds to the surface of an elementary Catastrophe. This allows smooth parameter changes to lead to a discontinuous change in the Bayes' decision. In future work this approach will be used to help develop a number of hypotheses concerning command processes and military headquarters structure. It will also be used to help capture such command and control processes in simulation modelling of future defence capability and force structure.     

Scenario analysis via bundle decomposition

Scenario analysis, originally proposed by Rockafellar and Wets, is a widely applicable method for introducing uncertainty into practical decision problems. As it often leads to very large optimization problems, one needs special techniques for the resulting numerical computation. One such technique, the Progressive Hedging Algorithm, is simple and universally applicable, but it can be slow. In this paper we show how the bundle decomposition method can be applied to linear or convex scenario analysis problems that are loosely coupled. We illustrate its effectiveness by presenting computational results for military force planning problems and for multi-scenario network models of production planning.The research reported here was sponsored by the National Science Foundation under Grant CCR-9109345, by the Air Force Systems Command, USAF, under Grants AFOSR-91-0089 and F49620-93-1-0068, by the US Army Research Office under Contract DAAL03-89-K-0149 and Grant No. DAAL03-92-G-0408, and by the US Army Space and Strategic Defense Command under Contract No. DASG60-91-C-0144. The US Government has certain rights in this material, and is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon.     

Problems and methods in partial differential equations

Summary The method of singularities is used to solve theCauchy problem for simple hyperbolic partial differential equations, namely, the wave equation and the damped wave equation. The representation formula for the solution of theCauchy problem is written in terms of finite parts and logarithmic parts of certain divergent integrals. A process of analytic continuation is also used to solve theCauchy problems under consideration. However, to obtain explicitly the representation formulas for the solutions, one must actually perform the analytic continuation. It is shown that this is best achieved by making use of finite and logarithmic parts. Simple examples were purposely chosen so as to show that consideration of finite and logarithmic parts is naturally unavoidable and ? in the very nature of things ?. To Enrico Bompiani on his scientific Jubilee. This work was sponsored in part by the Air Force Office of Scientific Research of the Air Research and Development Command, United States Air Force, through its European Office.     

Cost effective scheduling of imperfect inspections in systems with hidden failures and rescue possibility

Motivated by real-world critical applications such as aircraft, medical devices, and military systems, this paper models non-repairable systems subject to a delay-time failure process involving hidden and fatal failures in two stages during their missions. A hidden failure cannot cause the system to stop functioning while a fatal failure causes the entire system loss. The system undergoes scheduled inspections for detecting the hidden failure. In the case of a positive inspection result, the system main mission is aborted and a rescue operation is started to mitigate the risk of the entire system loss. The inspections are imperfect and may produce false positive and negative failures. We propose probabilistic models for evaluating performance metrics of the system considered, including mission success probability, system survival probability, expected number of inspections during the mission, and total expected losses. Based on the evaluation models, we formulate and solve an optimization problem of finding the optimal inspection schedule on a fixed mission time horizon to minimize the total expected loss. Examples are provided to demonstrate the proposed methodology and effects of key system parameters on system performance and optimization solutions.     

A global optimization method for the design of space trajectories

The problem of optimally designing a trajectory for a space mission is considered in this paper. Actual mission design is a complex, multi-disciplinary and multi-objective activity with relevant economic implications. In this paper we will consider some simplified models proposed by the European Space Agency as test problems for global optimization (GTOP database). We show that many trajectory optimization problems can be quite efficiently solved by means of relatively simple global optimization techniques relying on standard methods for local optimization. We show in this paper that our approach has been able to find trajectories which in many cases outperform those already known. We also conjecture that this problem displays a “funnel structure” similar, in some sense, to that of molecular optimization problems.     

Modeling,analysis and performance evaluation for fault diagnosis and Fault Tolerant Control in bottle-filling plant modeled using Hybrid Petri nets

In this paper, an approach to achieve fault diagnosis and Fault Tolerant Control in a typical bottle-filling plant using event based techniques is discussed. For this purpose, the plant is modeled using Hybrid Petri nets which enable study and analysis with regard to the working of the plant. Once effective modeling is done based on two different case studies considered, new algorithms are proposed to achieve fault diagnosis and Fault Tolerant Control on the models developed. Finally, performance measures with regard to the models proposed are evaluated to check the correctness of the models developed. Both analytical and numerical results are obtained which are highly useful to understand plant behavior.     

Discontinuity in decision-making when objectives conflict: a military command decision case study

In previous work, we considered the representation of human decision-making processes in closed-form simulation models of conflict. An important element of this representation is the rapid planning process that embodies the processing of information for situation assessment to support a course of action decision (eg in a military headquarters). The application of this work is in support of operational analysis models for defence procurement and balance of investment. This paper describes the application of non-linear multi-attribute utility theory in conflict scenarios in order to extend the representation of the rapid planning process to account for a wider set of subjective attributes of the decision-maker. The results show, through examination of experimental data, that decision-making can be modelled through a particular class of utility functions. These utilities embody a geometry which allows us to classify the types of decision being made when there are conflicting objectives and when decision-makers adopt very different and subjective appraisals of constraints and beliefs in outcome. The experimental results help to demonstrate that the subjective nature of the situation assessment, and the personality, training, experience and history of the decision-maker are central to the functional representations. This paper presents a way to capture this deeper representation of human decision-making in a way that is potentially useful for quantitative modelling using the rapid planning process as a basis.     

An Interactive Aid to Financial Planning

The problem of producing financial plans for the subsidiary of a multinational corporation is one which is critical, especially in uncertain times. In addition there is always a pressing time constraint. This paper describes a suite of models which are in use by Caltex Oil South Africa to assist in this area. The approach adopted was to provide simple models which work interactively. The financial planning analysts were able to be very involved in the project because a high level modelling language was used which they could easily comprehend. The tremendous benefits of this type of approach in terms of user acceptance and in other areas are examined.     

Factor models in high-dimensional time series—A time-domain approach

High-dimensional time series may well be the most common type of dataset in the so-called “big data” revolution, and have entered current practice in many areas, including meteorology, genomics, chemometrics, connectomics, complex physics simulations, biological and environmental research, finance and econometrics. The analysis of such datasets poses significant challenges, both from a statistical as well as from a numerical point of view. The most successful procedures so far have been based on dimension reduction techniques and, more particularly, on high-dimensional factor models. Those models have been developed, essentially, within time series econometrics, and deserve being better known in other areas. In this paper, we provide an original time-domain presentation of the methodological foundations of those models (dynamic factor models usually are described via a spectral approach), contrasting such concepts as commonality and idiosyncrasy, factors and common shocks, dynamic and static principal components. That time-domain approach emphasizes the fact that, contrary to the static factor models favored by practitioners, the so-called general dynamic factor model essentially does not impose any constraints on the data-generating process, but follows from a general representation result.     

A Model of the Dynamics of the Third World War — An Exercise in Technology Transfer

The paper discusses two scenarios for a Third World War in Europe, and argues that it is often convenient to supplement a scenario by a formal model. The problems of creating such a model are examined, and a model is formulated for land, air, and sea combat in Europe and the Atlantic, using the System Dynamics approach, which has not hitherto been widely employed for military analysis.The model confirms the results of the two scenarios, and its use for the analysis of alternative force configurations is illustrated. Some implications in the interpretation of Soviet military literature are discussed, and the paper ends with speculations on the role and value of such models.     

An analytical approach to the facility location and capacity acquisition problem under demand uncertainty

This article presents an analysis of facility location and capacity acquisition under demand uncertainty. A novel methodology is proposed, in which the focus is shifted from the precise representation of facility locations to the market areas they serve. This is an extension of the optimal market area approach in which market area size and facility capacity are determined to minimize the total cost associated with fixed facility opening, variable capacity acquisition, transportation, and shortage. The problem has two variants depending on whether the firm satisfies shortages by outsourcing or shortages become lost sales. The analytical approach simplifies the problem considerably and leads to intuitive and insightful models. Among several other results, it is shown that fewer facilities are set up under lost sales than under outsourcing. It is also shown that the total cost in both models is relatively insensitive to small deviations in optimal capacity choices and parameter estimations.     

Fractional dynamics of populations

Nature often presents complex dynamics, which cannot be explained by means of ordinary models. In this paper, we establish an approach to certain fractional dynamic systems using only deterministic arguments. The behavior of the trajectories of fractional non-linear autonomous systems around the corresponding critical points in the phase space is studied. In this work we arrive to several interesting conclusions; for example, we conclude that the order of fractional derivation is an excellent controller of the velocity how the mentioned trajectories approach to (or away from) the critical point. Such property could contribute to faithfully represent the anomalous reality of the competition among some species (in cellular populations as Cancer or HIV). We use classical models, which describe dynamics of certain populations in competition, to give a justification of the possible interest of the corresponding fractional models in biological areas of research.