The Elemental Resource Model

The Elemental Resources Model (ERM) developed by Kondraske (Kondraske, 2006a) was developed to address the problems with linear models in predicting human performance. The ERM is a performance based model built on the more general concepts of General Systems Performance Theory (GSPT). The major elements of the theory relevant to this discussion will be presented here, while the reader is directed to (Kondraske, 2006a) for further details.

The basic concept behind the ERM is that human-sub systems can be considered to provide a finite set of basic elements of performance resources (BEPs) that are drawn upon when performing higher level tasks. As Figure 4-6 shows, the ERM is based on the concept of Mondaology or the existence of a finite set of such elemental units that combine into the complexity that is seen in the world. The level of performance on a given high-level task is therefore determined by the limiting lower-level BEPs i.e. humans performing high-level tasks may utilise many of these lower-level resources, but any one of these resources could be a limiting factor in the performance of the high-level task. This results in a resource economic view of human performance.

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Figure 4-6 ERM model and the concept of Monadology (used with permission)

The following principles of the ERM are worth noting (Kondraske, 2006a):

The relationship between structure, function and performance: The distinctions between structure, function and performance are important. A system’s structure enables its function, and is defined by its purpose or what the system exists to do. Systems can perform more than one function, though it is likely that only one function will be executed at a given time.

Performance is a measure of how well a given system can execute its function. For example, it can be measured in terms of strength, speed or any other measure of capacity that can limit the system in task performance. The human system can be decomposed into a hierarchical structure down to a level where individual functional units serve a single function (e.g. elbow flexor: structure, elbow flexion: function). Each functional unit is characterised by a

multidimensional performance space (Kondraske, 1988). Therefore, a basic element of performance (BEP) is defined by a functional unit and a dimension of performance e.g. visual

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memory capacity. By definition, a performance resource always starts at 0 and increases. The entire set of BEPs across all human functional units forms a finite resource pool, or a

multidimensional performance envelope, from which an individual draws upon when

executing a higher level task. The amount of performance resource drawn is dependent on the task to be performed, and task success results when all required resources are available in sufficient amounts.

Resource availability versus resource utilisation: There is also an important distinction to be made between resource availability and resource utilisation. Resource availability (such as maximum visual acuity, contrast sensitivity, locus of visual field etc.) could be measured with various lab-based measures requiring maximum exertion. However, in the performance of a real world task, an individual might utilise these resources to different degrees depending on the task at hand. This resource utilisation requires that measurements take place while the actual task is being performed, making measurement more difficult to capture and less generalisable. It would be expected that resource utilisation would be highly correlated with overall task performance, but the measures that are available and commonly used are those of resource availability. Kondraske argues that linear combination methods (using linear regression) of measured resource availabilities therefore do not capture the non-linear threshold effects predicted by resource economic models, and it is the reason for the marginal performance of linear, statistically based models.

Resource economic mathematics: Mathematically, a resource economic model depends on the comparison of a set of resource availabilities (RA) with a corresponding set of demands (RD) on those availabilities derived from the high level task in question. By comparing the availabilities to the demands (RA ≥ RD) for each resource in the set of BEPs, and combining the results using a logical AND operation, a prediction of high-level task performance could be derived. This method checks that the demands of the high-level task fall within the user’s multidimensional performance envelope and then returns a negative result if any particular basic resource is found to be a limiting factor. This resource economic model has the added advantage that it can be applied at different hierarchical levels in a generic modelling strategy.

Figure 4-7 shows in summary form the distinction between linear combination and resource- economic models. In the top of the diagram, a linear combination model assumes that different proportion or weights (b1, b2, b3 etc.) of low-level resource availabilities (RA) combine to give the level of performance on a high level task. In the bottom of the diagram, a resource economic model assumes that the high level tasks can be broken down into a set of resource demands (RD1, RD2, RD3) that correspond to a set of resource availabilities (RA1,

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RD2, RD3). An estimate of high-level performance can obtained by checking that all the availabilities are equal to or greater than the demands of the high-level task. If one resource availability, in this case RA3, is less than the demand RD3, the AND condition is not satisfied and RA3 becomes the limiting resource.

Linear Combination Model : a + b1 x RA1 + b2 x RA2 + b3 x RA3 High Level

Task

RA1 RA2 RA3

RD1

RD2 RD3

Resource Economic Model:

(RA1≥RD1) AND (RA2≥RD2) AND (RA3≥RD3)

* Ra3 is the limiting resource High Level

Task

RA1 RA2 RA3

RD1

RD2 RD3

Figure 4-7 Linear combination models and resource economic model compared

A metric for performance capacity stress could be calculated which is the ratio of a resource demand (RD) to a corresponding resource availability (RA) i.e. RD/RA * 100. The threshold for a particular resource will be 100%, with values less than 100% indicating the amount of stress on a performance capacity, and values over 100% indicating the extent to which the resource demand exceeds the resource availability. Reserve capacity could also be defined as the difference between resource utilisation and resource availability i.e. RA - RD, given that RA > RD.

Thus, using the ERM involves determining the set of basic resources that can predict higher level task performance to form the basis of an evaluation method. This implies that it is more important to capture the wide range of low level resources than a few resources in depth, because this increases the probability of finding the limiting resources (Kondraske, 2006a).

Resource substitution: To explain human adaptability and coping strategies, Kondraske suggests the principle of resource utilisation flexibility with resource substitution. In this case, a performance resource may be substituted for another with the same dimensionality.

Human resource subsystems will act in such a way as to minimise the stress on all resources while maximising reserve capacity. This real-time multidimensional optimisation of

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performance resources practically leads to the scenario where individuals with different resource profiles could accomplish a task by using different procedures.

Kondraske describes a practical implementation of these ideas via Nonlinear Causal Resource Analysis (NCRA) (Gettman et al., 2003 ; Kondraske, Johnston, Pearson, & Tarbox, 1997). In this method, a range of BEPs are measured, and users then perform a high level task of interest. Instead of looking at correlations and regression models between BEPs and overall performance in the high level task, the NCRA methodology analyses the relationship between each BEP and overall performance and fits a curve to the lower boundary of points in each plot to yield a resource demand function (RDF). This RDF therefore represents the minimum required amount of a BEP in order to achieve a certain level of high level performance. This method therefore differs from linear methods of model building by recognising the non-linear relationships inherent in the combination of BEPs as they interface with real-world tasks.

Resource variation with time: The concept of dynamic diversity was discussed in Chapter 2, and refers to the changing of resource capacities with time. This essential element is also built into the ERM where a person’s performance envelope changes with time. This change can be brought about suddenly via disease, injury or trauma. It can also change progressively with time via the ageing process. In some cases, it can also vary on a day to day basis for

conditions such as arthritis where joint pain and stiffness reduce performance resources such as strength, speed and range of motion.