Unravelling Complexity Tim's Learning Portfolio

29 October, 2010

Week 11 – Role of Law in Unravelling Complex Evidentiary, Moral and Social Issues

Filed under: — Tim @ 1:22 pm

Panel

Simon Rice spoke about why the law is complex. Judy Jones told us about risk regulation, particularly related to environmental risks.

Law tries to:

  • anticipate social need
  • balance (recognised) interests
  • be consistent
  • be economically sensible
  • protect the vulnerable
  • conform to expectations
  • reflect morality
  • be comprehensible

Panel Question

There was no time allocated to questions today. Since we were discussing risk regulation I would have been interested to explore the similarities and differences in the understanding of risk by lawyers and actuaries.

Tutorial Reflection

In preparation for the tutorial we read about how opinions are formed in the US Supreme Court.

The useful part of the approach is having one person write a draft document very early in the process. The draft is a starting point for further debate and can be built on to form the final document. This will be a useful method to use for our policy brief assignment.

Connections within this course

During the role playing activity I got the impression that Simon didn’t know how to explain the technical issues around 3G mobile technology. It is of concern that people who make laws may not understand the technology they are trying to regulate. This can result in vague and difficult to interpret regulations. It would be better to specify the interference from phones in terms of their power and what part of the spectrum they use. This point is related to “understanding the underlying physical reality”, which I have mentioned previously in relation to the seminars I attended. In fairness to Simon, he was deliberately using a vague definition so that he could introduce 4G later in the exercise and I don’t think he is actually writing laws about this technology.

Connections to other courses

Many fields use the same definition of risk.

Risk = consequences  x  likelihood

The real question is, how are consequences and likelihood quantified? The typical approach of estimating (guessing) a value for each factor is somewhat unsatisfying. There are actuarial methods to quantify these values but the methods work best when there is good quality data about past experiences. Some complex issues relate to risks that have not yet been realised, such as the potential future impact of climate change. I have not found the solution to this yet, but I intend to pursue it further.

External Connections

Is there a causal link that means that the hub of technical innovation in the US (Silicon Valley), is about as far as you can get from Washington DC (which seems rather stifling)? There must be a balance, between over regulation and anarchy, where innovation thrives.

Tools to Manage Complexity

  • write a draft and then improve it

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