Doing More With Less

28 Oct

Whenever you read a government strategy and economic outlook for companies across the West, there is a consistent underlying theme-we need to do more with less. This is particularly the case with health and social care within the UK, where competing priorities and “doing more with less” are leading to increased pressures in service delivery and the search for new ways of working. Any opportunities which may emerge from examining and testing how to do more with less will first be met with inevitable resistance, as the natural human process of loss aversion takes hold. Then there is the flip of loss aversion, over optimism, as a strategic idea is used to explain away contradictions, obstacles and difficulties; this can lead to view that doing more with less is going to be far easier than the reality will allow.  I’m going to take a brief look at a potential method of tackling loss aversion and over optimism whilst aiming to boost the improvisation required to “do more with less”.

Kahneman and Tversky (1992) identified a human bias toward preventing and feeling a loss as opposed to securing gains. This means that when a person has something removed from them (or the threat of removal), they will feel the loss more and fight harder to avoid the loss, than the person would over any gain. Therefore, the mere notion of “doing more with less” invokes a strong psychological reaction. The optimism bias, is associated with the more abstract task of strategic planning (see Kahneman and Tversky, 1979 for original research). The designers of plans have a potential bias to overestimate their ability (and the ability of their available resources) to complete a task, whilst underestimating the difficulties, both anticipated and unanticipated, in derailing a task.

As a result, human beings are demonstrably poor at forecasting (Tetlock and Gardner, 2015) consistently and significantly over weighting or under weighting the impact of future events. This can trap people into a cycle of emotional (as opposed to intuitive) decision making, predicting “less” will be relatively easy to cope with or “less” will be impossibly difficult. The reality may exist in a large space in between, and not remain in that space for very long.

Kahneman and Klein (2009) observe that forecasting improves if the future outcome is already known. In the absence of knowing the future the next best solution is to imagine one. However, the imagined future must take the form of catastrophe. For example, imagine you have been asked to do more with less, and it is now 6 months into the future. The request has resulted in a complete catastrophe. Now take 5 minutes to write down the history of that catastrophe. Then ask what could you do? What links could be made? What software, online resources could be levered? Which partners or colleagues could help? And what would you need to let go of? This exercise enables leverage points and cross links to be located. It also separates what is realistically less versus impossibly less.

The above can be carried out in teams and groups, and findings should be shared to highlight potential innovation and high risks. The exercise enables people to spot previously hidden risks and previously hidden opportunities, it also shares a lot of learning and ideas. This leaves a question- why focus on catastrophe, a worse-case scenario? Dealing with worst case scenarios are far from the average experience, as a result people draw from their most testing previous experiences and the reservoirs of their knowledge. The result should highlight both experience and creativity.

Reading

Kahneman, D. Tversky, A. (1992). “Advances in prospect theory: Cumulative representation of uncertainty”. Journal of Risk and Uncertainty 5 (4): 297–323.

Tetlock, P. E. & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown

Kahneman, D. Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 80, 237–251

Kahneman, D, Tversky, A. (1979). “Intuitive prediction: biases and corrective procedures”. TIMS Studies in Management Science 12: 313–327

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