[DADM] Weekly Reflections – Week 1 ‘Introduction and the Nature of Decisions’ P2

Sheldon Cooper on the difficulties of decision making in today’s world


  • This video reminds me of The checkout (would highly recommend!) which, in this particular video it demonstrates the disconnect between the research and the common consumer and how not only are consumers fighting against biases but there are huge corporations capitalising on our mental weaknesses and biases to influence our decision making (this is just on normal goods, letalone the scary world of credence goods! https://www.youtube.com/watch?v=TawjGSwkb3c) https://www.youtube.com/watch?v=AH9jK_D_xzY

    • At 2.25 they actually discuss that when consumers have too much choice they tend to give up and not purchase anything. They can rely on ‘easy outs’ or heuristics (remind: thinking fast and slow – shortcuts to decisions) to make a decision. Also a sneaky tip of the hat to Dan Ariely and a study that appears in Predictably Irrational at the 3:00 minute mark!

There are a number of interesting insights from this video. I can remember being constantly paralysed by making decisions. Which phone plan, which internet plan, which phone to buy etc etc. Going through the information funnel from reviews + friend recommendations to narrow down a few options, then comparing specs side by side. Often making decisions on the smallest, perhaps irrelevant matters. BUT over time I have been able to make quicker decisions.

A point of inspiration was a video from the checkout show on the ABC which mentioned ‘good enough is almost always good enough’. So these days when making decisions it is my heuristic (which is not always true of course!) that when I have narrowed down to a few options, to just make a choice rather than extend the non-decision making process too long.

Predictably Irrational Animated book review + We’re All Predictably Irrational

(https://www.youtube.com/watch?v=gONHKwx7_aM) + (https://www.youtube.com/watch?v=JhjUJTw2i1M)

  • I have enjoyed reading his books and watching his videos and have used them in my own teachings of micro-economic courses. Especially when we think about the cost-benefit principle, I have often challenged my students to say ‘well this rule sucks because look at these experiments by Dan Ariely’ and then immediately defend the rule by saying ‘well, a deeper understanding of the rule would say that Dan is enforcing the rule and is serving as a lesson that there are often many costs and benefits that we are not aware of and it is very difficult to try and place dollar values on them to make decisions’
  • One key insight to take out of it is that everything is relative. Hence why understanding negotiation techniques such as framing, anchoring, high-balling etc are incredibly useful for things beyond negotiation itself. In decision making in corporations there is a negotiation of ideas, there is conflict, there is disagreement on the very facts that exist.
  • I really enjoy the study of market norms vs social norms. Understanding this has allowed me to be more honest with how I allocate my time. I feel that in smaller organisations with good company culture, this social norms factors in to the overall productivity and output of the business. Although doing work and not getting paid is illegal and not something I would ever consciously allow myself to do, if you enjoy working for your boss and have a connection to your team, these things do happen. It is very difficult to track such things but I suspect the impact of social norms affecting the market norms of my own workplace is not zero.

I find it also quite interesting when economists especially espouse people for doing things for a very low rate and considering this a fault in not understanding the value of our time. The example given here is lining up for 2 hours for a free ice cream that is worth $3. Indeed this puts our market rate at $1.50 but we must consider this in the context of opportunity cost and hence other opportunities at this time. Let us say that you did this Saturday morning at 10am to 12pm. Even if you are a consultant during the week for $50 an hour, this Saturday morning time is no valued at that, especially if you cannot claim extra work outside of hours for your normal rate. The typical argument is ‘well you could have gotten a job at mcdonald’s or something for $12 an hour’ or a job at X for $Y an hour. Is that realistic though? I take two issues with this (A) Although we are moving that way in the on demand economy, you can’t really just pick up work for 2 hours or 1 hour or 20 minutes. This leads to (B) You would likely have to pick up another proper job and the time cost associated with this as well as the mental cost of working all week then working more on the weekend might just make this a worse option. A full, realistic analysis of all the costs and benefits of that waiting in line for 2 hours may find there really is no other viable opportunity cost that makes this a bad option.

Overall – perhaps statements like this fall into the fundamental bias/issue discussed in these videos. That for those two hours you are comparing your hourly rate with your normal weekly hourly rate or the national ‘minimum wage’ which are not realistic comparisons.

A realistic view on decision making from Henry Mintzberg

  • This video mentioned a three part triangle of science, craft and art.

This reminds me of the knowledge art experience from DVN and how, indeed, there is a role for improvisation to play but there is need for a toolbox that can assist. Experience, however, is what will help deploy the various tools on the fly.

  • The video also categorises decision making as one of three options:
    • Analysis first, seeing first, doing first (iteratively)

The last reminds me of gradient descent algorithm as well as the agile methodology from project management. It appears that decision making, as a field of study, also benefits from the idea of ‘do a little bit, check it out, change the plan and go again’ just in the same way project management does and so does algorithmic approaches to machine learning.