The choice model proposed here and illustrated left is also the sort of thing which is used, inter alia, in procurements in large organisations. Essentially a rectangular array with columns for options and rows for features. Typically:
The first option – Option 1 – will be the do nothing option; leave things as they are. No need for action, perhaps because nothing better is on offer. Otherwise the ‘as is’ or the null option.
A small number of options in total, N in what follows and not usually more than five.
A rather larger number of features, M in what follows and not usually more than twenty in this context – although noting that models of this sort can have much larger numbers of features.
Features which might be regarded as mandatory, desirable, flaws, threats or opportunities. Or just plain interesting. Most of this can be captured in a numerical weight, as here, a weight which might be positive or negative - but notice the slightly different presentation of the mandatory features in the illustration. They are not scored in the way of the others, rather, for the option to fly, they just have to be present. A slight complication in the scoring code mentioned below.
The scores might be words like ‘good’ or numbers like ‘10’. We assume the presence of some code underpinning the model which can aggregate such feature level scores into option scores, with the option score being, in essence, the weighted sum of the feature scores. Also known as the scalar product. Something things are arranged so that the maximum score is 10 or perhaps 100. Sometimes scores will be negative. When times are bad, all the aggregate scores might be negative and the best that we can do is select the least negative: that is to say, arithmetically the largest, which is simple enough to code.
Plus trimmings.
Note that the list of options and the list of features is apt to evolve as one, as the brain, goes along – evolution which is often not allowed under the public procurement rules: no moving of the goalposts after the starting gun has been fired! We don’t need to be so strict in this context.
We do not suppose that the whole of the model which follows is going to be conscious at any one time. But any part of it could be made conscious, typically a whole row or a whole column. But large parts of it might be exhibited outside the brain, perhaps on some kind of a display screen. These kind of props are useful and make possible choice arrays of a size which would otherwise not be possible – and difficult to talk about with others.
Pseudo code
Declare expression – a bracketed expression, for example ‘run(David with=hammer on=‘road to Cambridge’ because=walk(Mary from=Oakington))’. Something like a simple version of html. Data which:
Has been organised a bit, some of the ground work had been done and the data is accessible to conventional computer code, for example the Visual Basic available under the hood of Excel.
Has a hierarchical structure which allows more or less unlimited complexity – while also allowing stuff which is simple and easy.
Might well include pictures, video clips, audio clips and other stuff derived from the five senses.
Declare options as array [1:N] of:
- option_name as expression – name being a convenience for reference
- option_description as expression – description being something a lot more substantial, probably including data from various senses
- tone as single – which might be positive, zero or negative. Tone is the brain’s more or less instant reaction to the option in question, based on its past experience and expressed in terms of feelings and emotions, which feelings and emotions might be positive, neutral or negative. Condensed here into a single number: what might, in other contexts, be called a knee-jerk reaction. One of the inputs to the score subsequently computed by the ego in slower time. An input which is sometimes helpful, sometimes not so helpful, but one which is apt to be given more weight when the id is in a hurry.
- aggregate_score as single – the sum of the weighted scores, with weights taken from the next section.
Declare features as array [1:M] of:
- Feature_name as expression
- Feature_description as expression
- Weight as single – which might be positive, negative or zero. We allow zero for the case where we thought that a feature was relevant but have decided, for the moment, that it is not – without wanting to discard the data.
Declare scores as array [1:N, 1:M] of score as single – non-negative reals. The extent to which this option exhibits this feature. Sometimes one requires the score to be a whole number between 0 and 5, 0 and 10, or something else of that sort.
Notes about the illustration
An incomplete blend of information from the options, the features and the scores arrays. We have used the top rows for information about options and the left hand columns for information about features, with the scores in the body of the worksheet. Incomplete in the sense that I have not captured everything in the illustration which I have put into the pseudo code. Completion is left as an exercise for the reader.
As was allowed in the foregoing, we have used labels rather than numbers for the scores. There would be some rule in the background which converted labels to numbers in some sensible way.
We use the convention that option 1 is what we have now, in the jargon of consultants, the ‘as is’. In this illustration, ‘as is’ has a high score, but not the highest. So we might think it worthwhile carrying on with the procurement.
Note the use of the special weight ‘mandatory’, together with the special scores ‘present’ and ‘absent’. The score of any option which fails a mandatory feature is here given a score of zero. If one was being pedantic, one might say that options which failed a mandatory should not appear in the model – while I prefer a bit more flexibility, flexibility which, for various reasons, some good, procurement people are not keen on.
In Excel there is no particular limit to the number of rows – that is to say features – or the number of columns – that is to say options: it will allow a lot more of either than we are likely to want here.
Reference 1: http://psmv3.blogspot.co.uk/2016/09/what-is-consciousness-for.html.
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