Tinker, Tailor, Experiential Learner

How do we gain our knowledge and wisdom about compensation and total rewards?  How do we come to understand the conditions in which a bonus plan is likely to succeed or fail?  How do we learn to read the landscape so as to hone in on the base pay practices and program (or information and communication) elements likely causing employees to feel that their salaries are unfair?

In my last time up on the Cafe circuit, I wrote a post about the movement towards evidence-based rewards management coming our way.  Another interesting and provocative point of view about learning crossed my path while working my way through Nassim Taleb's book Antifragile, and I thought it worth tossing out here for consideration and conversation.

Taleb distinguishes two models, or sets of beliefs, about how we gain skills and insights: The first focuses on the kind of knowledge you acquire in school (or through reading and research) and the second on the learning we get by doing or that comes to us naturally (through innate biological instinct).  He makes the case that we tend to overestimate the role and necessity of the first type - academic knowledge - and degrade the "uncodifiable, more complex, intuitive or experience-based."  Not only that, but we are also inclined to erroneously attribute many things we learn via the latter to the former. 

He presents his version of this widely-held, ostensibly top-down model, as follows...

Academia --> Applied Science and Technology --> Practice

... and illustrates what he terms the lecturing-birds-to-fly effect with the following story.

Think of the following event:  A collection of hieratic persons (from Harvard or some such place) lecture birds on how to fly.  Imagine bald males in their sixties, dressed in black robes, officiating in a form of English that is full of jargon, with equations here and there for good measure.  The bird flies.  Wonderful confirmation! They rush to the department of ornithology to write books, articles, and reports stating that the bird has obeyed them, an impeccable causal inference.  The Harvard Department of Ornithology is now indispensable for bird flying.  It will get government research funds for its contribution.

It also happens that birds write no such papers and books, conceivably because they are just birds, so we never get their side of the story. ... Nobody discusses the possibility of the birds' not needing lectures - and nobody has any incentive to look at the number of birds that fly without such help from the great scientific establishment.

Taleb laments the fact that "history belongs to those who can write about it", which makes us blind to the possibility of an alternative learning process, one which really operates as a loop:

Random Tinkering --> Heuristics (experience-based techniques, aka rules of thumbs or common sense) --> Practice and Apprenticeship --> Random Tinkering --> Heuristics --> Practice and Apprenticeship

In short, Taleb would warn us about overreliance on formal education, research and what the "experts" teach us -- and underreliance on what we learn through tinkering, tailoring, practice and apprenticeship. 

Does this position fly in the face of the notion of evidence-based reward management?  I don't see it that way.  I believe we must hold fast to the notion of tinkering and practice; if anything, becoming more rigorous (i.e., evidence-based) in how we do our experimentation-on-the-job.  Because, let's be honest, that spanking new incentive plan that you just rolled out is an experiment.  How are you going to maximize your learning and insights from the experience?  More than that, how can you share your ground-level lessons with fellow practitioners -- and gain the benefit of theirs?

Because that is how we innovate and move practice forward.  That is how we develop and get better at compensation and total rewards.

That's what I think.  You?

Ann Bares is the Founder and Editor of the Compensation Café,  Author of Compensation Force and Managing Partner of Altura Consulting Group LLC, where she provides compensation consulting services to a wide range of client organizations.  Ann was recently named President Elect of the Twin Cities Compensation Network (the most awesome local reward network on the planet) and has joined the Advisory Board of the Compensation & Benefits Review, the leading journal for those who design, implement, evaluate and communicate total rewards.  She earned her M.B.A. at Northwestern University’s Kellogg School and is a bookhound and aspiring cook in her spare time.  Follow her on Twitter at @annbares.

Creative Commons image courtesy of jking89

Comments

[Dan Walter]

This is one of your best articles Ann. Great perspective. Excellent work at applying a general concept directly back into the comp world. Most importantly, a great reminder that evidence is not simply data from outside sources. The best evidence is the information you collect as you pay attention to what you are doing (and have done).

[Guvenc Altas]

Great article made me feel the power of observation,talking with people who really owns the subject we are talking about.Evidences are out of offices and mostly comes with a mixture of facts,emotions and perceptions.I think we can succeed as we are capable of filtering each of them before jumping on conclusions.Let's learn from birds more.

[Ann Bares]

Dan:

Thanks! And your point is an important one - remembering that evidence (maybe some of the best) is gleaned by paying attention to the outcomes and lessons of your own work.

Guvenc:

Yes - let us learn more from birds! Thanks!

[Mercedes McBride-Walker]

Great post, Ann. I think it is important to pay much more methodical attention to what's in front of us, observing, analyzing, and developing from the new learning. I also think it's a both/and with academia. I think there are great studies with great information for Total Rewards practitioners out there. I do agree, however, that they may be somewhat inaccessible. This is a good reminder for 'both sides': that is, academicians be sure to identify practical implications of the study so that they are useful results, and practitioners be rigorous and disciplined in how data is being observed and analyzed to have credible data for better informed decisions.