Our world, our economy and our industry are going through significant change. As actions are being taken on a macro and micro scale, there are arguments for and against the benefits of these actions being played out every day. It got me thinking about whether we devote enough time and effort to understand and model the causes and effects of what we decide to do. On a global basis we have seen the dramatic impact of the economic crisis and much has been written about the root causes of that situation including the Turner report that we have written about in our blog earlier this year. Without oversimplifying the situation, it was interesting that the original intention and effect of securitisation was a positive reduction in risk to the original lenders. The eventual effect [unintended consequence] was a significant increase in risk being placed upon our institutions and our economy. So, one would hope that looking at causes and effects of problems should help us to learn for our future plans and actions. Indeed, we can use cause-and-effect diagrams (known as fishbone or Ishakawa diagrams) to model the effects of a particular problem.
Whilst these are useful, the question that I am looking to address is how we get better foresight and so try to model and identify what are the possible future effects – intended, unintended unforeseen consequences – of a chosen action. Should we assume ‘Newtons law’ (pleasure – pain?) of there being an equal and opposite reaction for every action? Some would argue this will be the case with our current global stimulus strategies where we are simply staving off one set of problems to suffer another set of problems in the future. If this is the case, has anyone really sat down and mapped out the interdependent actions and reactions that flow from what we are doing now? In reality, the situation is likely to be very complex and urgency is often a justification for not doing such an analysis.
There are a range of mathematical models including the ‘Chaos Theory’ (the behaviour of certain dynamical systems to sensitivity) that look are deterministic and random modelling to measure certain outcomes including how small (fractional), apparently insignificant changes can have far-reaching disproportional effects. For example ‘the Butterfly effect’ which asks if the flapping of a butterfly’s wings on one continent can create a hurricane elsewhere. There is a so-called law of ‘unintended consequences’ which is affected by the world’s inherent complexity, perverse incentives, human nature (and stupidity), self-deception, failure to account for other cognitive or emotional biases. Robert K. Merton (the man who coined the phrase ‘self-fulfilling prophecy’) listed five possible causes of unanticipated consequences:
1. Ignorance – we don’t know what we don’t know (it is impossible to anticipate everything, thereby leading to incomplete analysis)
2. Error (incorrect analysis of the problem or following habits that worked in the past but may not apply to the current situation)
3. Immediate interest, which may override long-term interests
4. Basic values may require or prohibit certain actions even if the long-term result might be unfavourable (these long-term consequences may eventually cause changes in basic values)
5. Self-defeating prophecy (fear of some consequence drives people to find solutions before the problem occurs, but the fact that it is no longer a problem is unanticipated!)
If we look at the causes of the economic crisis, we can see how a number of these elements played a part. Indeed, if we ignore some of the political ‘point scoring’ currently taking place, the actions that have been taken to address the crisis and the consequences that flow from them are being debated in a number of short and long term – usually negative – scenarios. It has been said that the use of very clever mathematical modelling in banking has been a cause of some of the problems. However, if we are better able to apply effective modelling to improve outcomes, this should be a good thing and we should avoid sweeping conclusions such as ‘all maths is bad’ or that ‘all bankers are bad’. Nevertheless, we must be sure that the use of such modelling techniques are understood and tested for sanity rather than being followed blindly.
We have seen an increasing use of ‘deterministic’ and ‘Monte-Carlo’ (stochastic) modelling in Financial Planning software in order to help customers to see the effects of action or inaction on their own future circumstances. Whilst we should not overcomplicate things just for the sake of it, there is no doubt that such tools can help advisers and customers if used and understood properly; for example funding properly for retirement and lifestyle protection. Equally, there are people out there that simply want to make a decision based on what they can afford within a limited budget. Getting the balance between sophistication and potentially high cost of advice versus simple and cheap is important. Too much emphasis on the former could result in people not making any provision at all, rather than doing something that may be inadequate, but is still better than nothing.
So, should we expect to benefit from serendipity, or should we ensure that we understand and assess our current and future needs then put a plan of action in place to deal with what we know and expect could happen. Perhaps we should also take into account Mr Murphy’s law too and so expect the unexpected!
