Computer-based Simulation Modelling for Anthropologists Michael D. Fischer

Simulation Home

Introduction

Uses

Definitions

Approaches

Design

Implementation

Evaluation

Examples

Bibliography

 Approaches to Simulation

 There are a number of approaches to simulation depending on the purpose to which the simulation is intended, the models available, and the information available. However, the general form of a simulation is fairly regular. Simulations are almost always used to validate, explore or extrapolate the properties of a model of some system. The basis of simulation is modelling the behaviour of a model, and thus it is possible to have a very simple simulation consisting of a single model operation applied to a single model object (or structure) .  However, a simulation is usually a model of a system consisting of at least two sub-models. which interact either by one being directly influenced by the other, direct mutual influence, or where both operate on at least one common object (Figure 2).

 One general motivation for simulation is that we may lack the means to substantively describe or characterise the interaction of these sub-models using either prose or formal devices, but we can observe their interaction case by case. Simulation is then suitable for those situations which are beyond our normal means to model in a concrete fashion. It is notable that simulations are widely used in applications of the physical sciences in engineering, for although these disciplines have very good models for describing the local interaction of variables, they too, in general, lack the means to describe or predict the interactions that a complex system might have. Thus airplanes are not built on the basis of first principles of physics, but are designed using  these principles, and the designs are refined, first using computer simulations, and then physical simulations such as a wind tunnel.

 There are two basic types of simulation components, stochastic and deterministic, which roughly correspond to Levi-Strauss's distinction between statistical and mechanical models (Levi-Strauss 1965).

 Stochastic simulations generally have a probabilistic component, though not usually unconditioned probabilities (eg sampling from the normal distribution, poisson distribution or even catastrophe spaces where the probability density changes with different paths or other spaces, for that matter). Stochastic simulations generally must be performed a number of times until there is an adequate sample of results to evaluate, since any single 'run' of a simulation will have one of many possible outcomes. This sort of simulation is often used as a component in demographic or ecological simulations where many of the components are modelled using statistical criteria. They are especially useful where much of the data upon which they are based has been amenable to statistical analysis, or where the local behaviour of many systems is only understood in statistical terms, or where it is convenient to model many of the components using statistical or probabilistic models, which is desirable much of the time, even if it is not in theory necessary. It is, for example much easier to model rainfall or warfare with a simple probabilistic model (sampling from an empirical distribution), rather than going to the trouble of an elaborate model which is itself extremely complex, simply because we want to estimate the impact of floods or the loss of male population due to warfare as an independent context for our central problem.

 Deterministic simulations are those where one solution set exists for a given input situation. It primary use is extrapolate and evaluate outcomes given hypothetical inputs, or to examine the interaction of a number of interdependent deterministic models. Depending of the form of the form of the model used, it may not be by some definitions a simulation proper, but I am including any animation of models within this category.

 Polyvalued simulations have more than one outcome for a given starting point, but probability plays no identifiable part in the multiplicity of outcomes (either due to lack of knowledge or because we are not concerned with probability); there are simply many outcomes, representing operations which are not functions. With this type of simulation we are usually concerned with the set of solutions as a whole. This kind of simulation could be used to explore all the possible marriages that might exist in a specific population, and the different structural outcomes of each marriage. This allows us to at least determine the boundaries of a system.  It is useful in situations where a number of different rules could apply in a given context.

 What can we gain from using computer simulations in our research? Simulation is appropriate where we can reasonably model the circumstances and context of some complex behaviour, and want to explore and evaluate models of that behaviour. For example, we are sometimes concerned with the plausibility of some practice of some stated rules or preferences, say a preference for marriage to FB{SD}. One of the ways we can explore a system of this sort is to examine some model under various known circumstances to compare our own data to. In the above case we can model a population demographically, state some rules, preferences, and conditions for marriage, and examine some of the outcomes of applying these to the model population. It is important when using simulation as a part of analysis to avoid two errors posed by Dyke (1981:202-3). His methodological error refers to the process of elaboration of simulations to improve their 'realness' to the point that it is difficult to ascertain the impact any of the simulation elements. His heuristic error refers to concluding that 'life' mimics the simulation. The fact that a given simulation might conform very closely to observed situations does not argue that these operate on the same principles, only that there is some degree of logical similarity between the processes in the simulation and the processes of the simulated system (Dyke 1981:203). Figure 2. Distributed simulation model.

Qualitative Simulations

 Often in social anthropology we have data which is impossible to quantify or sample, where we cannot give precise counts or parameters, or even meaningful probabilities. Because of the nature of collecting ethnographic data, data is recorded as it arrives, and only contextual information can be systematically collected in a form that could represent a proper sample from which a probability could be estimated. However these qualitative collections are the essence of ethnographic collection. They apparent serve most of the needs of social anthropologists, and we do not tire of attempting to analyse them. Most simulation techniques are based on numerical and/or statistical criteria, and make little sense in analysis of marriage rules, sections, totemism or dreams.

 It is possible to simulate using only qualitative objects and structures, although the use and evaluation of these simulations are somewhat different, and in many ways more complex than quantitative simulations, especially with respect to validation. In a qualitative simulation we have at best categories as parameters (although these may be expressed based on a probability), and the simulation focuses on the relationships between categories and objects within the simulation; where the structure is as important, or more so, than the content.

 There are two basic approaches for qualitative simulations. The first is fairly conventional, which simply consists of transforming an input structure into an output structure. This corresponds to the usual design of a quantitative simulation. The second approach is by resolution. With resolution we are simulating a system of rules and conditions, and using the simulation to answer specific questions by resolving whether, given the information in the simulation, a specific situation can occur. In the former, we alter the input structure to generate different output structures, and evaluate these output structures. With resolution, we hypothesise different possible output structures, and the simulation tells us whether they are possible or not within the model of the simulation.