Human Behaviour Modelling as an Emerging Disruptive M & S Technology

Understanding social and organisational phenomena is important in structuring modern military organisations and in conducting modern military operations. Simulations incorporating human behaviour modelling are an important tool for developing such understanding. In this paper we survey some of the successes of simple human behaviour models, and outline the key factors in making more complex simulations epistemologically valid (e.g. valid theoretical constructs and justified selection of model parameters). Finally, we summarise some necessary steps for developing a true science of modelling and simulation for human systems, and mention some recent progress in this vein, such as work in the modelling of emotion. 1.0 INTRODUCTION Modern military operations often have goals which are explicitly social in nature. Such operations include peace-keeping and operations other than war (OOTW), as well as warlike operations such as counterinsurgency, where success is measured largely in terms of social effects. Outcomes of such operations cannot be assessed solely with the physics of kinetic-energy weapons. While physics-based simulations may be adequate to assess whether a hostile armoured division can be destroyed by a planned operation, physicsbased simulations are insufficient for assessing planned counter-insurgency operations or OOTW. Where social effects are critical, simulations of human behaviour within society are necessary to assess the likely outcomes of different courses of action and to provide appropriate training to personnel. Furthermore, over-simplistic social simulations will not be sufficient for this purpose, since they will make inaccurate predictions. For example, some simulations of social behaviour in Iraq and Afghanistan [1, 2] have used the “Hierarchy of Needs” developed by Abraham Maslow [3, 4]. This theoretical construct suggests that human beings seek to satisfy five different kinds of needs in priority order: physiological needs, safety needs, love and belonging, esteem, and self-actualization. However, Dunne [5] suggests that Maslow’s hierarchy, being inherently individualistic and “Western” in nature, is “non-deployable” to countries like Iraq, where the needs of the family, clan, or tribe often outweigh personal needs. Social simulations are not only important for assessing the targets of military operations and OOTW, but they can also be usefully applied to one’s own country. In particular, social factors within one’s own country will determine the size and nature of the recruitment pool from which military personnel will be drawn. The size of the future recruitment pool determines future staffing levels, and therefore the degree of automation necessary in future military forces. In addition, the skills of the future recruitment pool will determine future training and doctrine policies. It is therefore important that both these factors are well understood. In addition to modelling one’s own society as a whole, it is also important to model one’s own military organisation. Disruptive technologies, particularly information and communications technologies, affect the way that current forces operate, and provide options for new ways of operating. Edge organisations [6] are Human Behaviour Modelling as an Emerging Disruptive M&S Technology 22 2 STO-MP-MSG-111 one well-known example. Organisational simulation is one important tool for assessing the strengths and weaknesses of alternate organisational structures and alternate ways of operating. In order to benefit maximally from disruptive information and communications technologies, it is important that the evolution of organisational structures, procedures, and tactics be guided by organisational simulation. 2.0 SIMPLE HUMAN BEHAVIOUR MODELS For many applications, simple models of human behaviour have proved adequate in supporting decisionmaking. Dirk Helbing and others have successfully used very simple behaviour models in simulating emergency evacuation dynamics [7, 8]. Such models have shown, for example, the effectiveness of zigzag designs for evacuation passageways, and have helped to improve the safety of the annual Hajj in Saudi Arabia. The Hajj involves large numbers of pilgrims visiting specific locations, and this has led to injuries which have been addressed by both physical and organisational changes [7, 9]. Relatively simple human behaviour models incorporating economic decision-making have had success in predicting fluctuations in prices of various products [10, 11]. Combining models of disease biology with simple models of people’s movements during the day allows the simulation of the spread of infectious disease [12, 13, 14]. Such models are helpful in setting policy on vaccination, quarantine, health education, and other public health issues. Similarly, physical models of rainfall etc. can be combined with simple models of decision-making about land use and crop-planting. This allows the prediction of agricultural outcomes, both in a historical context [15, 16, 17] and in guiding policies about present-day land use [18]. 3.0 EPISTEMOLOGY OF HUMAN BEHAVIOUR MODELS Simple models of human behaviour have proven successful partly because they are simple. Simple models are easier to calibrate to real-world data than more complex models, as well as being easier to validate and verify. Provided that they are based on valid theoretical constructs, simple models are more likely to succeed than more complex models, although simple models naturally have less ambitious goals. Obtaining useful and valid results from more complex models of human behaviour requires paying attention to the epistemological aspects of simulation – that is, paying attention to applications of the theory of knowledge [19, 20, 21]. The traditional definition of knowledge is justified true belief [22]. In the context of simulations, this means that the output of the simulation can be traced back to empirically or theoretically justified knowledge of human behaviour, via a series of steps which are also justified [21]. In particular, we require: • valid and appropriate theoretical constructs (Maslow’s “Hierarchy of Needs” and Freud’s psychological theories being examples of constructs of questionable validity); • appropriate empirical data, validly collected through surveys [23], experiments, observation, video analysis, instrumentation [24], role-playing games [25], wargaming [26], etc.; • valid mechanisms for translating empirical data into model parameters; • valid and tested model-building techniques [27]; and • verification and testing of the final model. When one or more of these steps cannot be performed, the simulation model cannot be considered to produce knowledge (though it may still be a rich source of interesting hypotheses to be tested some other way). Human Behaviour Modelling as an Emerging Disruptive M&S Technology STO-MP-MSG-111 22 3 3.1 Theoretical Constructs The theoretical constructs used in models of human behaviour should represent the “most prominent causal relations” in the behaviour under consideration [28]. Valid theoretical constructs exist for individual human beings, but agreed-upon constructs with predictive validity are difficult to find for society as a whole. However, the emergence of social simulation models is driving research in this area. 3.2 Data Collection Data collection methods are most advanced for individual humans and small groups. When modelling societies and other large groups of people, empirical data on individuals remains useful. However, it must generally be supplemented by surveys and other methods which collect data on society as a whole. The advent of communications technologies such as the Internet and Twitter also permits data to be collected electronically. For example, Google search trends can reveal information about disease outbreaks [29]. 3.3 Model Parameters There are two main approaches to translating empirical data into model parameters. Where parameters correspond directly to measurable quantities, it is possible to work forwards from the data. This has been done in, for example, epidemiological simulations. When doing this, care must be taken to avoid errors due to discretization effects [21]. It is also important to conduct sensitivity analysis, in order to determine how much the model depends on precise measurement of the empirical data [30]. The other approach to parameterisation is to work backwards, by comparing model outputs to corresponding measurable quantities. Parameters must then be adjusted so that the model gives the best possible fit to the data [21]. This requires searching a potentially enormous parameter space, and genetic algorithms or other sophisticated search strategies can assist with this [31]. This approach also presupposes a unique best-fitting parameter combination. This is more likely to occur if multi-objective optimization is used – that is, if the model output is required to fit several independent real-world data values [21, 32]. 3.4 Testing Simulation models must be verified to see whether they match their design. Testing is an important part of such verification [33, 34, 35]. It is important that test data not have been previously used for parameter selection, to avoid predictively useless models that simply replicate their calibration data [36]. Selfexplaining agents complement traditional testing methods [37]. A self-explaining agent can output reasons for its actions, so that an agent’s history can be presented as a “story. ” Unrealistic decisions in such a “story” can reveal implementation errors [21]. Plotting average values and distributions of model variables is also an important testing technique. 4.0 THE EMERGING SCIENCE OF SIMULATION We are beginning to see an emerging science of modelling and simulation [38] that allows the production of models which produce epistemologically valid results, although it remains the case that the “need to develop simulations o