Voices from Accenture Public Service

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We make decisions every moment of our lives – based on our values, intuition and insight. While we think carefully about important decisions, is there a better way?

Research shows that we tend to make a decision and then rationalise to ourselves – we can find flaws in our rationalisation sometimes but we are engineered to find a way to justify our choices, so our tendency is to stick with our original point of view.

In most cases our insight is fine – yet there are numerous examples where our intuition does not work. We are particularly challenged when using our intuition for complex decisions – our natural understanding of statistical significance is far from perfect.

There are many tools available to help us make decisions – for example SWOT analysis, Pareto analysis, thinking hats, decision matrices – these are aids to help us think about the decision and give us some insight that may challenge our intuition.

In this digital age we have an opportunity to make better use of data and computing to help us with our decision making. Yet this will be a radical shift in how we think about decision making – we will need to understand data, and understand how insight from data can inform our decision making.  The techniques are mathematically more challenging for us to understand.

The first technique – Bayesian Inference – was developed many years ago and gives a mechanism to develop probabilities for a certain outcome based on statistical analysis. The approach is to start with an ‘a-priori’ assessment of the outcome (which is subjective). We then do research/gather data about the decision, and apply the results of the analysis to our decision probabilities to re-assess our position. The benefit of the approach is that multiple factors can be assessed and applied to our decision in a way that is likely to result in an non-intuitive answer.

The second technique – Causal Reasoning – is an evolution of both Bayesian Inference and statistical correlations. It draws on the insight that statistics does not model cause – and we all know the risk of correlation implying causation. Yet correlation may have an underlying rational causation and it is this causation that can give us deeper insight. Developing a causal network that is based in some rational deduction about the nature of cause and effect will allow us to develop better insight into data and thus inform our decisions.

There may still be some subjectivity in the assessment and indeed there can be un-intended bias in the data we use. The model we choose for the casual network may be flawed, or our choice of data for analysis maybe both flawed and biased. Hence these techniques are no panacea that take away our accountability for making decisions.

Artificial Intelligence research is starting to embrace these ideas, as is research into consciousness and our models of human intelligence – understanding how we think will no doubt improve our ability to augment our thinking.

These new approaches for making decisions will need us to embrace data and statistical analysis with casual or Bayesian models.  There is still a strong human component, yet we can make better use of the data and our insight into how the world works to improve our decision making.  Governments and companies have yet to embrace these techniques – but those that do are likely to make better decisions. Deep analysis of critical issues can only bring value and quality to our decisions – even if we choose to do something different we do so with knowledge and insight.

References

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3150852/

https://ieeexplore.ieee.org/document/4680669/

https://en.wikipedia.org/wiki/Decision_theory

https://en.wikipedia.org/wiki/Bayesian_inference

https://en.wikipedia.org/wiki/Causal_reasoning

https://www.nytimes.com/2018/06/01/business/dealbook/review-the-book-of-why-examines-the-science-of-cause-and-effect.html

 

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