Causal models

There are no subsidiary control patterns to this one in this book.

13 Causal models

Example: Suppose a local authority is making plans to deal with municipal solid waste over the coming decades and needs to look at all its options. They develop

Targets Step A Step B Step C Step D etc.

Completeness 0.5 1.0 0.2 0.6 1.0

Accuracy 1.0 0.5 0.2 2.0 1.0

Validity 1.0 1.0 0.2 0.2 1.0

Uniqueness 0.5 1.0 1.0 2.0 1.5

Differences Step A Step B Step C Step D etc.

Completeness -0.5 -0.5 0.5 -0.6 -0.8

Accuracy -1.0 0.5 2.8 -1.0 1.0

Validity -1.0 -1.0 0.6 -0.1 -0.2

Uniqueness 0.0 -1.0 -1.0 -2.0 -1.0

C A V U Step A Step B Step C Step D etc.

Control 1 0.5 1.0 1 1

Control 2 0.2 1.0 0.7 1 1

Control 3 0.5 1 1

Control 4 1.0 0.1 1 1 1

etc.

Table 6.7 Matrix mapping calculations

a model of the waste creation and disposal showing the causal links they believe are relevant to them. The model is drawn using boxes and arrows and is partly quanti ed. They use it to understand what they are still uncertain of and how important it is, and also to explore alternative strategies. This includes actions they can take to learn more about how waste works so that they can manage it better.

CAUSAL MODELS are one of the most rigorous and informative ways to understand uncertainty and examine the impact of alternative controls. They support CAUSE

-EFFECT INTERVENTION and should often be the ultimate form reached through

EVOLVING UNCERTAINTY LISTS.

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Uncertainties are not separate from each other or from other thinking.

Suppose a business strategist is thinking deep, strategic thoughts about products, markets, and returns. Future growth in potential markets is almost certain to be one of the important factors to consider. It will be listed in documents, perhaps shown as a bubble on a ‘‘strategy map’’ or other drawing of the planning problem.

If the strategist is using a numerical model there will be a spreadsheet with cells for market growth rates and formulae to combine those rates with other information and provide projections of business results.

But, of course, those market growth rates are uncertain. They are uncertainties that matter, which makes them the same as risks in most interpretations. In non-mathematically-based approaches to risk management risks like this would be described as something like ‘‘Risk of insuf cient market growth’’ but the idea on which this is based is simply the uncertainty about market growth. Similarly, a risk register item like ‘‘Risk of competition’’ comes from uncertainty about the ‘‘Intensity of competition’’ and ‘‘Risk of being hit by a meteor’’ is from the uncertain variable

‘‘Meteor impacts’’.

In this way ‘‘risks’’ are necessarily linked to our mental models of how things work and what would happen if we took alternative actions. Risks are the uncertainties around those models.

Not surprisingly ‘‘risks’’ are also connected to each other via the mental models.

You can experiment with existing risk registers to understand how this works.

The items on a typical risk register are linked to one or more consequences that may or may not have found their way onto the register. Often many register items have at least one link to another item on the register. Some are within loops and some of those loops are vicious/virtuous circles.

Situations where the risk register items are largely separate from each other are rare. Connections are much more common.

For example, a typical group risk register I examined recently gave the usual high-level survey of risks across the business. (A project risk register would have seemed more closely linked even without diagramming.)

The original register contained 31 rows. From the text I identi ed a total of 41 nodes (i.e. variables in a mental model of the business and its environment) and designated 30 of these as being the direct counterpart of a risk register item.

The remaining 11 nodes were causes or effects also referred to in the risk register item text. I added all the causal links between nodes directly implied by the text or obvious from general knowledge.

Using this procedure I identi ed 107 links, of which 100 involved at least 1 risk register item and 51 had a risk register item at both ends. All the risk register items were linked into the network. The picture of the network shows the nodes to be massively linked to each other.

Of the 30 risk register items on the causal network, 22 had a direct causal impact on one or more other risk register items and 17 were directly caused by another risk register item (at least in part). Also, 17 of the risk register items had other risk register items as both causes and effects.

Fourteen of the 30 risk register items were part of at least one loop, while 21 of all 41 nodes were involved in at least one loop.

In summary, the risk register items were highly interlinked, with around half involved in causal links with other risk register items, most of these being part of loops. There were about three times as many links as risk register items.

Experiments like this suggest that directly estimating impact on results of interest is very dif cult because:

links between variables are common;

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the properties of these links are often highly uncertain –– we don’’t know how

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strong they are or what time delays may be involved;

many things that might happen are linked to impacts we care about by one or

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more steps of causality whose strength is uncertain; and

many links create loops that either amplify or reduce the impact of some

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unexpected event, making impact estimation particularly dif cult.

This implies that we should try to model these links and carefully assess our uncertainties. This is not always simple to do and it is easy to end up with a lot of boxes and arrows on a diagram and no time to do anything with them.

Therefore it is important do work iteratively, starting with the variables we regard as the results we value and want to in uence, and working back little by little to the variables that in uence them and that we might be able to control.

The reward for this effort is a much more complete and controllable view of risk with scope for moving towards greater detail and quanti cation over time. Alternative controls can be added to the model to consider their potential impact.¹

If there are important connections between uncertainties –– and there usually are –– then it is easier to put them on a picture than to list ‘‘risks’’ and pretend the connections and the underlying mental models do not exist.

In summary, models explicitly showing causal links de ne one possible analysis of risk and provide outstanding insight into risky situations. The apparent complexity compared to risk registers just re ects the fact that the links are made explicit and can be controlled by working iteratively. Therefore:

Capture knowledge about how things work in a causal map (diagram). Add knowledge about areas of uncertainty derived from the map: current values of variables, future values of variables, properties of links, the structure of the model itself, and which variables to put value on and how. Develop actions from these and other sources and consider including them within the model. Do all this iteratively based on insights from each version of the model.

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CAUSAL MODELS do not create the controls so need to be combined with CAUSE

-EFFECT INTERVENTIONS,GENERIC CONTROL DESIGN LIBRARIES, or CONTROL PATTERNS. They can also support RISK REGISTERS. When CAUSAL MODELS can be quanti ed it is possible to use M^ONTE CARLO SIMULATION to understand the combined impact of uncertainties. A speci c style of CAUSAL MODEL useful in some situations is FAULT AND EVENT TREE ANALYSIS.