Making Robust Decisions
Making good decisions consistently is a skill that can separate successful individuals and organizations from their unsuccessful counterparts. In addition to making good decisions, they need to be made in a timely manner, otherwise dependent tasks will grind to a halt.
There are a number of well documented key ingredients to a good decision making process such as being made on facts and data, identifying the owner of the decision, having a no blame culture, etc. This article, however, is not concerned with these attributes of a good decision making process. Instead, this article concentrates on the thoroughness of the decision making and proposes a process called aspect splitting.
Let’s start by taking a look at what can happen if you don’t split the aspects of a decision. Without aspect splitting, the core of the decision process involves the identification of the pros and cons of each of the choices. Here is a fictitious example based on choosing a house from two choices:
At first glance, the analysis seems to break down the decision into separate aspects, however, more scrutiny of the example reveals different aspects are considered for the different options. Essentially, the pros and cons are not comparable.
This approach is extremely common. It often leads to an incomplete analysis of each of the options and may result in important considerations being omitted. In the case above, House B seems to have more positive aspects so it would seem to be the best option.
To overcome this problem, the important considerations should be identified and considered against one another to ensure the analysis is comprehensive. Each consideration is referred to as an aspect, hence the name aspect splitting.
Structuring the decision in a tabular format with separate rows for each aspect provides the visual cues to ensure every combination of choice and aspect are considered. A tool such as Roobrick also allows scores to be attribute on a per aspect/choice combination such that an overall score for each choice can be computed automatically.
The following example illustrates the effect of splitting the decision into aspects and attributing scores:
Notice that all of the aspects were considered in the first example (without aspect splitting), they were not all considered for both choices. The second example does, however, consider all of the aspects for both choices, thereby forcing the contributors to make a more thorough decision. In the example, some less appealing aspects for House B, such as fewer bedrooms, result in a poorer overall score and a deviation frblog-robustom the initial decision.
Obviously not all aspects are equal, so it is sometimes necessary to assign different weights to aspects. Extending the previous example, aspect weights are shown under the aspect titles in the following illustration:
As can be seen in the above example, the weight of each aspect affects the combined score of each choice and results in a further departure from the initial, non aspect oriented based decision.
The degree of aspect splitting appropriate to a decision obviously depends on the importance of the decision. It could be argued, however, even trivial decisions can be dissected into at least a few aspects quickly and a cursory evaluation of each aspect/choice combination is still likely to result in a more robust decision without sacrificing speed.
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