Illinois Investment Network


Recent Blogs


Pitching Help Desk


Testimonials

"I wish to thank the Dealflow Investment Network for their splendid service on listing our project summary. Our entire fund raise was achieved within 5-months from China. Long flight, but well worth it. I am happy to give a recommendation."
James E. Mack

 BLOG >> Recent

Stopping Rules [Decision Making
Posted on March 9, 2017 @ 11:37:00 AM by Paul Meagher

A stopping rule is used to determine when one should stop searching for things like a spouse, parking spaces, investment deals, a new home, a new secretary, etc.... The "look then leap" stopping rule suggest that we should just look for awhile so that we increase the likelihood of encountering the optimal spouse, the optimal parking spot, the optimal investment deal, the optimal house, the optimal secretary, etc... The question is how long we should keep looking before deciding to leap?

A considerable amount of research has been done to find an optimal strategy for determining when we should stop looking. It turns out that we should stop looking at secretary applicants after we have interviewed 37% percent of them. After that we should jump at the next secretary that is better than the previous secretaries we interviewed. If we are looking for a marriage partner, then we should figure out how long we are prepared to look for that partner and once we have used up 37% of that time, we should consider proposing to the next marriage partner that we regard as better than the ones we have been with to date. The 37% rule applies to either the number of items to be searched or the amount of time we have to search.

If you use this optimal strategy then 37% of the time you will pick the optimal item you are looking for. There is no optimal stopping rule that gives you certainty that you will pick the optimal item. The best investment deal may have been in the 37% of deals you reviewed to date and didn't make an offer on or perhaps if you waited until you reviewed 60% of the deals you would have found the optimal deal. If you set your optimal stopping rule at some number other than 37%, however, your chance of finding the optimal item will be less than 37%. That is all that optimal means in this context.

This form of the optimal stopping rule makes alot of assumptions so whether it is applicable or not depends on your particular situation. For example, if you are allowed to go back and pick the best secretary of the 37% you have interviewed, or if the secretary is allowed to refuse your offer, then the math behind the stopping rule changes and we would have a different optimal strategy for that situation.

The "look then leap" stopping rule also assumes that we are ranking items relative to each other (ordinal scale) rather than relative to some absolute scale (cardinal ranking). If we have some absolute criteria we can use to evaluate candidates then we can pick a candidate if they exceed some threshold we have set for selecting them. Using a "threshold rule" to determine when to stop is another stopping rule stategy we can use.

A "threshold rule" allows us to potentially finish our search faster than using the "look then leap" strategy. Instead of looking for who you might "love" the most by comparing each to the last, you instead set some criteria that your potential marriage partner must meet and as soon as the person meets those criteria you propose.

Stopping rules are important to determining when we should walk away from an investment. Those who lost everything during the 1929 Wall Street crash did not stop in time. Gerald Loeb pulled out before the crash and credited his stopping rule for his success in doing so: "If an investment loses 10 percent of its initial value, sell it".

There is also a rule when climbing Mount Everest that if you are not on the top by 2 o'clock then you should turn around. It does not end well for those who ignore this rule.

In my next blog on The Lean Startup book, I'll be dealing with the chapter titled Pivot and we'll see that this is very much concerned with knowing when to stop in your present course and when to persevere.

Stopping rules can be informed by mathematics and probability theory but can also involve general rules of thumb that have proved useful in the past. This discussion of stopping rules was inspired by Algorithms to Live By: The Computer Science of Human Decisions (2016) which focused on the more formal approaches to stopping rules, and Simple Rules: How to Thrive in a Complex World (2015) which focused on the rules of thumb that are used to guide our stopping decisions.

Permalink 

 Archive 
 

Archive


 September 2020 [1]
 June 2020 [4]
 May 2020 [1]
 April 2020 [2]
 March 2020 [1]
 February 2020 [1]
 January 2020 [1]
 December 2019 [1]
 November 2019 [2]
 October 2019 [2]
 September 2019 [1]
 July 2019 [1]
 June 2019 [2]
 May 2019 [2]
 April 2019 [5]
 March 2019 [4]
 February 2019 [3]
 January 2019 [3]
 December 2018 [4]
 November 2018 [2]
 September 2018 [2]
 August 2018 [1]
 July 2018 [1]
 June 2018 [1]
 May 2018 [5]
 April 2018 [4]
 March 2018 [2]
 February 2018 [4]
 January 2018 [4]
 December 2017 [2]
 November 2017 [6]
 October 2017 [6]
 September 2017 [6]
 August 2017 [2]
 July 2017 [2]
 June 2017 [5]
 May 2017 [7]
 April 2017 [6]
 March 2017 [8]
 February 2017 [7]
 January 2017 [9]
 December 2016 [7]
 November 2016 [7]
 October 2016 [5]
 September 2016 [5]
 August 2016 [4]
 July 2016 [6]
 June 2016 [5]
 May 2016 [10]
 April 2016 [12]
 March 2016 [10]
 February 2016 [11]
 January 2016 [12]
 December 2015 [6]
 November 2015 [8]
 October 2015 [12]
 September 2015 [10]
 August 2015 [14]
 July 2015 [9]
 June 2015 [9]
 May 2015 [10]
 April 2015 [10]
 March 2015 [9]
 February 2015 [8]
 January 2015 [5]
 December 2014 [11]
 November 2014 [10]
 October 2014 [10]
 September 2014 [8]
 August 2014 [7]
 July 2014 [6]
 June 2014 [7]
 May 2014 [6]
 April 2014 [3]
 March 2014 [8]
 February 2014 [6]
 January 2014 [5]
 December 2013 [5]
 November 2013 [3]
 October 2013 [4]
 September 2013 [11]
 August 2013 [4]
 July 2013 [8]
 June 2013 [10]
 May 2013 [14]
 April 2013 [12]
 March 2013 [11]
 February 2013 [19]
 January 2013 [20]
 December 2012 [5]
 November 2012 [1]
 October 2012 [3]
 September 2012 [1]
 August 2012 [1]
 July 2012 [1]
 June 2012 [2]


Categories


 Agriculture [72]
 Bayesian Inference [14]
 Books [15]
 Business Models [24]
 Causal Inference [2]
 Creativity [7]
 Decision Making [15]
 Decision Trees [8]
 Design [37]
 Eco-Green [4]
 Economics [12]
 Education [10]
 Energy [0]
 Entrepreneurship [65]
 Events [2]
 Farming [20]
 Finance [25]
 Future [15]
 Growth [18]
 Investing [24]
 Lean Startup [10]
 Leisure [5]
 Lens Model [9]
 Making [1]
 Management [9]
 Motivation [3]
 Nature [22]
 Patents & Trademarks [1]
 Permaculture [36]
 Psychology [2]
 Real Estate [2]
 Robots [1]
 Selling [11]
 Site News [19]
 Startups [12]
 Statistics [3]
 Systems Thinking [3]
 Trends [7]
 Useful Links [3]
 Valuation [1]
 Venture Capital [5]
 Video [2]
 Writing [2]