Of late, I’ve been thinking a lot about idealism versus reality (for a lot of reasons). I’ve been a staunch advocate for better learning science in practice (an idealistic stance). And, we’re running a conference because we’ve gotten feedback that folks wonder what that means in practice (reality). My own situation is a case in point as well. So, I’m doing some reflecting on idealism and reality.

To start, I’m a principled kind of person. I try to follow the best recommendations from what we know about how we think, work, and learn. Perhaps I err too much on that side, as I’ve avoided things like commissions and paid endorsements.  That’s because I want my recommendations to come from real value, not my personal benefit. Which should be better in the long term, but I’m also aware I’m not a great biz dev type. For perhaps the same reasons. (And, it appears in retrospect, that when I do sell myself, I do so far too cheaply!)

Despite myself, I’ve managed to be involved in some things I care about. People have come to me, and I’ve managed to support the family for the past almost quarter century(!). Yet, there’ve been good times, and lean. (In the latter, currently.) Yet I haven’t been one to jump on bandwagons, for instance the latest hype around Generative AI. You might think a voice of caution would be appropriate, but the evidence appears to be to the contrary. C’est la vie. I’m not intending to change my stance, just being aware and honest with myself (and, consequently, you).

Beyond my own issues, I see that our field still faces challenges. Perhaps from our origins – taking good performers and trying to turn them into trainers without sufficient preparation – we end up trying to meet unrealistic expectations. “Do it once, and it’s good enough!” Cheaply and quickly, of course. If we measured, we might know otherwise, but that’s still too rare. Everyone has faith that we’re ‘sufficient’.

Yet, as an idealist, I see what we could offer, if we could manage to turn things around. I am an optimist (despite any appearances as a curmudgeon to the contrary ;). We could be impacting organizational success with aptly targeted interventions. Moreover, we could be the ones guiding organizations to new insights that find opportunity from increasing change. And so I keep fighting for the principled view. AND, practical steps to get there. I keep hoping (idealistically) that there are those who want to steadily move to a better organizational position where they’re both doing well what they know they need to, and efficiently exploring the new opportunities to adapt to the changing environment.

And, frankly, that’s the opportunity I’m looking to offer. Of course, in reflecting on the realities, I recognize that people also need to find ways to do better within the existing constraints, and steadily (stealthily?)  move those constraints to a better place (reality). Having done so for pretty much all my many moons of a career, I do have practical steps around that. That, too, is what’s on offer. There are ways to balance idealism and reality. Stay tuned (or tap in!).

I’m not a leadership guru by any means. In fact, having read Pfeffer’s Leadership BS, I’m more of a cynic. However, I have been learning a bit from my LDA co-director Matt Richter (as well as CEO of E9, and leadership coach, David Grad). Matt’s a fan of Keith Grint, UK Historian, who talks about how you need to make decisions differently in different situations. His approach reminds me of another, so here I’m looking at contextual leadership.

Grint talks about three situations:

• Tame: where things are known, and you just manage
• Wicked: where things are fluid, and you need to lead a team to address
• Critical: where things are urgent, and you need to make a decision

The point being that a leader needs to address each objective appropriately to the type of circumstance you’re facing. Makes sense. We know these different situations arise.

What this reminds me of is Dave Snowden’s Cynefin framework (he’s very clear not to call it a model). Again, I’m not au fait with the nuances, but I’ve been a fan of the big picture. The main thing, to me, are the different situations he posits. That includes:

• Clear means we have known solutions
• Complicated likewise, but requires certain expertise for success
• Complex systems, which require systematic exploration
• Chaotic, and here you just have to do something 

As I understand it, the goal is to move things from chaotic and complex to complicated or clear. (There’s a fifth area in the framework, confusion, but again I’m focusing on the big picture versus nuances.)

So, let’s do a mapping. Here, I posit, tame equates to clear and complicated, wicked is complex, and critical is chaotic. Clearly, there’s a time element in critical that doesn’t necessarily apply in the Cynefin model. Still, despite some differences, one similarity emerges.

The important thing in both models is you can’t use the same approach to all problems. You have to recognize the type of situation, and use the appropriate approach. If it’s critical, you need to get expert advice and make a choice. If it’s not, but it’s new or uncertain, you assign (and lead) a team to investigate. This, to me, is really innovation.

The tame/clear, to me, is something that can and likely should be automated. People shouldn’t be doing rote things, that’s for machines. Increasingly, I’m seeing that we’re now getting computers to do much of the ‘complicated’ too, rightly or wrongly. We can do it right, of course, but there are times when the human pattern-matching is superior, and we always need oversight.

The interesting areas are the complex and chaotic. Those are areas where I reckon there continue to be roles for people. Perhaps that where we should be focusing our efforts. Not everyone needs to be a leader every time, but it’s quite likely that most everyone’s potentially going to be pulled into the decision-making in a wicked or complex situation. How we manage those will be critical, and that’s about managing process to obtain the best out of the group. That’s something I’ve been looking at for a long time (there’s a reason my company is called Quinnovation ;). Particularly the aspects that lead to the most effective outcomes.

So, we can automate the banal, manage the process right in innovating, and be decisive when things are time-critical. Further, we can select and/or develop people to be able to do this. This is what leadership should be, as well as, of course, creating the culture that the group will exist in. Getting the decision-making bit right, though, builds some of the trust that is necessary to accomplish that last bit. Those are my musings, what are yours?

 

 

 

A regular commenter (by email) has taken me to task about my recent post on cognitive science. Which is fair, I’m open to criticism; I can always learn more! Yet, I feel that the complaint isn’t actually fair. So I raise the debate here about our (post) cognitive nature. I welcome feedback!

So, the gist of the discussion is whether I’m positing a reductionist and mechanistic account of cognition. I argue, basically, that we are ‘meat’. That is, that our cognition is grounded in our physiology, and that there’s nothing ephemeral about our cognition. There is no ineffable element to our existence. To be clear, my correspondent isn’t claiming a metaphysical element either, it’s more nuanced than that.

What I am missing, supposedly, is the situated nature of our cognition. We are very much a product of our action, is the claim. Which I don’t dispute, except that I will maintain we have to have some impact on our cognitive architecture. Channeling Paul Kirschner, learning is a change in long-term memory, which implies the existence of the latter. For instance, I argued strongly against a view that all that we store from events is the emotional outcome. If that were the case, we’d have nothing to recreate the experience, yet we can recount at least some of the specifics.  More emotional content means more recall, typically.

The accusation is that I’m being too computational, in that even if I go sub-symbolic, I’m still leveraging a computational model of the world. Whereas I believe that our thinking isn’t formal logical (as I’ve stated, repeatedly). Instead, we build inaccurate and incomplete models of the world (having shifted from formal mental models to a more predictive coding view of the world).  Further, those models are instantiated in consciousness in conjunction with the current context, which means they’re not the same each time.

Which is where I get pilloried. Since we haven’t (yet) explained consciousness, there must be something more than the physical elements. At least as I understand it, and it’s not clear I do. Yet, to me, this sort of attitude seems to suggest that it’s beyond comprehension, and maybe even matter. Which I can’t countenance.

So, that’s where the discussion is currently. Am I still cognitivist, or am I post-cognitivist? I’m oversimplifying, because it’s been the subject of a number of exchanges, without resolution as yet. This may trigger more discussion ;). No worries, discussion and even debate is how we learn!

Two recent times, I’ve seen glorious stories of how things could be. And, to be fair, I’ve been guilty myself; I have pursued and purveyed rosy stories. Yet, as I recognize more of the world’s challenges – randomness, illogic, bias, money, and more – I begin to question myself. What is it about the garden path?

The usual story is something along the lines of ‘first this happens, and it leads to this, …, and then this wonderful thing happens.’ The transitions sound plausible, they could happen!  The causal story continues from good outcome to next good outcome, until we get the inevitable results. And, if we’re not careful, we might miss the problem.

There’s also the chance that the transitions won’t happen. Brian Klaas’ Fluke is one story that illustrates the role chance plays. Randomly, things don’t go as planned. Julie Dirksen’s Talk to the Elephant, talks about the ways our systems and people themselves go awry. There are many things that stand in the way of  things working out the way you expect or even intend. As has been said, never predict anything, particularly the future.  I once heard an analysis that says that the trends you observe do tend to continue, but something unexpected always flips them from where you thought things would go.

Another issue are the underlying assumptions. Often, they’re more unlikely than they seem. Will Y happen because X happened (e.g. will this person get the job offer because they rode up in the elevator with the hiring manager)?  Do you even accept the premise of the assumption? Just because someone tells you that the sky is green, are you going to believe them when your own experience may differ.

There are benign situations, and then some that are not.  When I have told such stories, I (sadly) believed them. I have been an idealist (and in many ways still am), so I inferred a world where things worked as planned. (I have learned better, for instance watching a promising enterprise be undermined by ego and greed.) Then there’re the more insidious ones, when someone’s telling a story to convince you to do something that is less likely than is portrayed. In either case, either the innocently naive or the venally misleading, are prevailing upon the gullible. And, of course, I’ve been victim on the receiving end as well.

What’s my point (he asks himself)? I guess that it’s to be wary of such stories. Don’t tread along the rosy trail portrayed without some assessment of the probabilities. Ask yourself if the final outcome is as plausible as the starting point would suggest? There’s lots of room for distraction as you trod the garden path. Be aware of claims that all will follow the same path!