170. Optimizing can be dangerous

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“Optimization hinders evolution.” - Alan Perlis

In a recent stock valuation session, we were discussing the broad area of capital allocation and specifically, the topic of distribution of capital back to the shareholders (via buy backs and dividends). Then we veered into the topic of cost of capital and subsequently to the nebulous nature of debt. Apart from being a cheaper source of capital than equity, debt further reduces the cost by letting companies offset tax outgo with interest payments. Many publicly listed organizations optimize their cost of capital by taking on debt. Taking debt seems like a good thing and more importantly, not taking enough debt seems like disservice to the shareholders. But is it really so? Many companies have gone bankrupt in adverse economic conditions just because they could not service their debt.

"Once you have mastered time, you will understand how true it is that most people overestimate what they can accomplish in a year - and underestimate what they can achieve in a decade!" -- Tony Robbins

In our personal lives, we take housing loan to buy a house. Leverage (total cost of the house divided by your down payment) is seen by some as a way to amplify gains if the house price were to go up. Of course, if the house prices were to come down, you end up amplifying your losses too. But this article is not on the double-edged nature of leverage. It is about how optimization is not always the right way to go. Say you are on a hunt to buy a house. You finally find one which seems to tick all the boxes of your requirements and then some. For example, you were looking for a two-bedroom apartment but found a suitable three-bedroom apartment instead. Or you found a suitable apartment in a better locality than what you were originally prepared for. But this extra advantage comes at a financial cost. Let us say that your bank is happy to lend you the additional funds. If you fully utilize your capacity to take on loan for the sake of additional advantages that the prospective apartment is conferring, in a way, you have optimized your resources. But this can get perilous if the future was to turn out adverse. If you get laid off, will you be able to handle your mortgage payments when you have no slack left in your finances.

‘If you optimize everything, you will always be unhappy.” - Donald Knuth

It comes down to a tussle between margin of safety (buffer) and optimization of resources. The balance between these two opposing qualities is essentially subjective and is affected by one’s personal circumstances and psychological disposition. Too much margin of safety may hold you back from your potential and too much optimization will expose you to untenable risks.

I think the biggest issue with trying to optimize our life is that we may end up occupying all our time with seemingly productive activities. Optimization limits you to marginal/incremental improvements and that is its biggest drawback. Paradigm shifts in productivity generally materialize from changing the things you do and how you do them – not from incremental changes.

“Nothing is less productive than to make more efficient what should not be done at all.” –?Peter Drucker

Sometimes instead of incremental improvements, blind efforts at optimizing life can lead to fundamental setbacks. For example, take the case of sacrificing sleep to schedule in additional “productive” activities. It is understood scientifically (read the book “Why we Sleep” by Mathew Walker for more details) that sustained lack of enough sleep may seriously impair health – sometimes leading to death as well. There is such widespread disregard for this basic human need that we seem to have now normalized seven hours of sleep a night to be an acceptable benchmark. According to Walker (although confident of this, I am writing from memory here) most of us need at least eight hours sleep per night – seven hours is just not good enough.

"Give me six hours to chop down a tree and I will spend the first four sharpening the axe." - Abraham Lincoln

As to why optimizing generally leads to suboptimal outcomes, it is because we mostly operate in the arena of complex adaptive systems (“Complex Adaptive Systems” again!). Reiterating what characterizes complex adaptive systems (CAS): large number of variables, interdependence among the variables, positive (compounding) and negative (constraining) feedback loops, dynamic and non-equilibrium environments, path dependence etc. Without getting technical on any of these aspects, it is important to note that in complex adaptive systems, analytical (formulaic) models do not hold sway – that means, it is not possible to determine beforehand the magnitude and direction of output, for a given input. Complex systems are not suitable for optimization. In fact, they cannot be optimized. Efforts of optimization may turn out costly and errant.

Does this mean optimization is not a worthy cause? No. Local or modular optimization is possible and should be sought. For example, efforts involved in cooking a regular meal can be optimized but not the menu for an entire month, particularly when more than one family member is involved. All activities with repeatable steps are suitable for optimization.

Bottomline

"Better to be three hours too soon, than a minute too late." - William Shakespeare

Optimizing may seem like the right thing to do – it improves outcomes and is also prized by society. But optimizing works only in specific situations. When things are ordered/static and variables are independent, you can be sure of incremental gains when you optimize the activities involved. But most things in life are dynamic and hence efforts to optimize may backfire. Even when it works, the efforts involved in optimizing may more than offset the benefits that accrue. In complex, dynamic environments, a better approach is to unearth the fulcrums – those small efforts that lead to large effects. This may be possible by approaching the situation with an experimental and curious mindset – that means, more of bias for small actions as opposed to planning bias and then being big on monitoring and modifying approach as needed – in other words, bias for “why” over “what”.

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