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Speaker 1: Welcome to stuff to blow your mind from housetop works

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Speaker 1: dot com. The next order of business, if it pleases

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Speaker 1: your Highness, is the issue of continued vandalism of the

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Speaker 1: castle's east wall more graffiti. Well, what does it say

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Speaker 1: this time? The details are not important, your majesty, but

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Speaker 1: suffice to say that that the work criticized certain royal

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Speaker 1: policies as well as the the Royal beard, the royal

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Speaker 1: the royal beard. Well, I never well, what are we

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Speaker 1: doing to combat the problem. We solve the west wall

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Speaker 1: graffiti issue, yes, my lord, but but we're working to

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Speaker 1: implement a constant God presence anti vandalism spikes and erratic

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Speaker 1: paint scheme is aline. Well it worked here before, it'll

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Speaker 1: work this. I'm well, yes, my Lloyd, but these solutions

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Speaker 1: merely prevent the physical vandalism of a particular stretch of

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Speaker 1: the wall at any given time. This is but a

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Speaker 1: tame or a benign problem, you know, Uh, the overall

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Speaker 1: issue of vandalism with the Kingdom. It's a it's a

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Speaker 1: wicked problem, a problem sorcery. Fetch the witch. I'm to general, No, no, no, no,

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Speaker 1: my Lloyd. Not so escery, not pervasiveness, complexity. We're talking

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Speaker 1: about a public policy issue here, one with roots and economics, law, religion,

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Speaker 1: and other areas. We can't simply pull up the weed,

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Speaker 1: because the roots are tangled throughout the soil, and indeed,

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Speaker 1: treating one underlying cause is likely to disrupt other areas

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Speaker 1: of royal interest, alienate supporters, or force us to face

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Speaker 1: unflattering facts about ourselves. The royal beard is above reproach. Certainly,

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Speaker 1: my lord, Certainly, a finer beard has never been grown

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Speaker 1: in God's creation, no question there. But what is in

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Speaker 1: question is the very nature of the problem. Is it

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Speaker 1: the mere physical act of vandalism? Is it the perception

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Speaker 1: of the crown, poverty, a lack of religion or education.

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Speaker 1: This is a wicked problem. Yes, my would the wickedest.

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Speaker 1: By the pricking of my thumbs, something wicked this way comes. Hey,

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Speaker 1: welcome to stuff to blow your mind. My name is

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Speaker 1: Robert Lamp and my name is Christian Seger. And as

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Speaker 1: you can guess from our little audio play at the

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Speaker 1: beginning there, we are talking today about wicked problems. Yeah,

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Speaker 1: this is a fascinating sort of overview topic. Um that,

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Speaker 1: and I wasn't really familiar with this terminology Yeah, I

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Speaker 1: wasn't either. I actually stumbled across this a couple of

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Speaker 1: days ago. In particular, I was one of the resources

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Speaker 1: that we're going to talk about today about mains sort

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Speaker 1: of political science approach to wicked problems popped up on

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Speaker 1: my radar and I read that and I thought, Wow,

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Speaker 1: this is a really interesting way for us to sort

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Speaker 1: of approach science for the show. And it's it's a

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Speaker 1: way that we don't usually talk about science, right, like

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Speaker 1: science podcasts usually have, like such a reverence for the

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Speaker 1: institution of science. Science is the great problem solved, or

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Speaker 1: it's the thing upon which we have built everything we

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Speaker 1: hold dear, It is the it is humanity's backbone in

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Speaker 1: a way. In a lot of ways, science is treated

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Speaker 1: in the same way as religion is by some people, right,

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Speaker 1: Like I know plenty of people who aren't religious, but

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Speaker 1: they turned to science as having the answers and and

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Speaker 1: and it's definitive for them, right. Uh. And this is

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Speaker 1: a really interesting way to approach that because it gets

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Speaker 1: into the deeper complexities of using science as a way

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Speaker 1: to solve the world's problems. Yeah, And it gets into yeah,

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Speaker 1: it basically deals with our inability or certainly are difficulties

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Speaker 1: with tackling complex problems, complex issues, um, throughout our our culture. Yeah, exactly.

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Speaker 1: So we're gonna try to approach you know, we're gonna

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Speaker 1: tell you what a wicked problem is, first of all.

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Speaker 1: But the way that we're going to try to approach

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Speaker 1: it here is sort of on this scale. There are

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Speaker 1: macro wicked problems which we're gonna talk about, which are

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Speaker 1: sort of like our large scale societal ills, I guess.

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Speaker 1: And then we're gonna talk about it in a relation

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Speaker 1: to science and the science community. And then we're gonna

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Speaker 1: bring it down to the human level and talk about it, uh,

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Speaker 1: you know in the way that it's most applied in theory,

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Speaker 1: which is in the workplace. Uh. And it's the model

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Speaker 1: of wicked problems is used, uh basically as like a

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Speaker 1: management technique. And you should probably should probably also take

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Speaker 1: a moment to discuss its ties to Boston area dialect. Yeah,

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Speaker 1: so okay, this is worth mentioning. And we played around

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Speaker 1: a little bit of the audio play I'm from New

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Speaker 1: England originally. Uh, And you know, most people probably don't

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Speaker 1: notice that because I tried not to affect my accent

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Speaker 1: on the air, but Whenever the word wicked is the

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Speaker 1: into something, it might seep out a little bit here there,

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Speaker 1: so you might hear me losing some rs here there,

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Speaker 1: or changing the way I pronounced things. As we're going through,

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Speaker 1: I'm gonna try to try to hold it together. Well.

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Speaker 1: I I admit that when I was reading the material,

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Speaker 1: I would about this, and reading some of the papers,

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Speaker 1: I would come to the phrase wicked problems, and I

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Speaker 1: would often hear hear it spoken in my head in

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Speaker 1: the voice of Julianne Moore's character and thirty Rock, which

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Speaker 1: he played the Boston She did a great job with that. Yeah,

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Speaker 1: it's it's either Julianne Moore or like uh, Mark Wahlberg

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Speaker 1: in The Departed, like doing his his best Dorchester accent. Okay, so,

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Speaker 1: wicked problem? What is it? You're probably wondering what the

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Speaker 1: heck are we talking about? We sort of introduced you

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Speaker 1: to the basic idea throughout that little uh the play

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Speaker 1: that we enacted. But here's the breakdown. A wicked problem

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Speaker 1: is a social or cultural problem that's difficult to solve

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Speaker 1: because of incomplete or contra dictory knowledge uh and usually

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Speaker 1: the number of people that are involved in the interconnected

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Speaker 1: nature of this problem is part of other problems, right,

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Speaker 1: so they're all connected. Uh, and often it's just written

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Speaker 1: off as too cumbersome to be something to bother with.

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Speaker 1: So this is gonna seem familiar to all of you

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Speaker 1: as especially uh citizens in the United States as we

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Speaker 1: are in the middle of a crazy the presidential cycle. Uh,

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Speaker 1: and all of these things are coming up and are

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Speaker 1: frequently being talked about with you know, in my opinion,

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Speaker 1: very little concrete answers because they're wicked problems, right yeah.

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Speaker 1: I mean it's basically a standard aspect of politics. Nobody's

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Speaker 1: getting up there and stumping and campaigning and saying, uh

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Speaker 1: on the on the topic of poverty. This is a

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Speaker 1: complex issue and we probably will not be able to

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Speaker 1: solve it. We're gonna throw our best minds at it.

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Speaker 1: But every time we try and fix it, we're probably

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Speaker 1: going to change the problem. Nobody's saying that. People are

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Speaker 1: saying I have a plan I have or I'm gonna

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Speaker 1: throw some really uh you know, classy people at it

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Speaker 1: to fix it. Uh. Nobody nobody is is campaigning um

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Speaker 1: on a platform of wicked problem. But I think like

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Speaker 1: the more mature approach and probably for some candidates maybe

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Speaker 1: once they're in office. The approach is, hey, look like

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Speaker 1: these are problems that are so big we will never

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Speaker 1: solve them, right, but we we can if we can

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Speaker 1: sort of understand them on a larger level like that

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Speaker 1: and use that framework, then we can approach them in

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Speaker 1: a different way that's healthier and maybe can make them better.

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Speaker 1: Maybe not solve them, but make them better. So we're

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Speaker 1: talking about poverty, sustainability, equality, health wellness, racism, are failing

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Speaker 1: education systems, terrorism, you name it. All that stuff falls

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Speaker 1: under sort of the rubric of wicked problems or multi

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Speaker 1: headed snake monsters in the swamp. Go back to our

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Speaker 1: hydro episode, because even the hydra, of course, the idea

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Speaker 1: of being that you every time you chop up ahead

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Speaker 1: two or more grown its place. Even Hercules, son of

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Speaker 1: a god, took on this task, and the best he

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Speaker 1: could do was limited to one undying head and just

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Speaker 1: sort of hide head under a rock, which is perhaps

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Speaker 1: a telling metaphor for like, even the best attempts to

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Speaker 1: tackle a wicked problem, all you can really do is

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Speaker 1: like clear cut and barry and and hope that people

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Speaker 1: forget that this was a problem. I guess I think

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Speaker 1: the hydro metaphor is gonna work well throughout this is

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Speaker 1: you know, I'm not quite sure what order we're gonna

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Speaker 1: release season, but yeah, so, Uh we also talked to

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Speaker 1: this week about hydros on a different episode, and hydras

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Speaker 1: are a great example for it, because you you can't

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Speaker 1: solve the hydro problem, right, you could at least the

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Speaker 1: mythic one. Yeah, you cut off one head, two more

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Speaker 1: heads grow to replace it, right, and so you But

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Speaker 1: I think maybe like the angle of wicked problems is

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Speaker 1: knowing what the two heads are that are going to

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Speaker 1: grow to replace it. Yeah, we're trying, yea, trying to

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Speaker 1: figure it out, or certainly just being being conscious of

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Speaker 1: the fact that complex problems are complex, that that the

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Speaker 1: that many of the issues that are are not going

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Speaker 1: to be easily tackled, and you're not gonna be able

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Speaker 1: to solve them with a quick application of this policy

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Speaker 1: or that policy. Uh, it's all you, That's why they

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Speaker 1: are wicked. And not to mention, you know, the theoretical

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Speaker 1: applications within the workplace. You know, I'm assuming most of

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Speaker 1: you out there listening have jobs or have had a job,

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Speaker 1: and know the frustrations that go along with that, and

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Speaker 1: really you can use the wicked problem model at that

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Speaker 1: level too, and I find uh that it gives like

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Speaker 1: a little bit of a sense of freedom and relief

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Speaker 1: when you think about it that way, the frustration of

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Speaker 1: employment issues. Yeah, and I think it's also important to

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Speaker 1: remember that the wicked problem is in contrast to a

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Speaker 1: tame or benign problem. The tamer benign problem is often

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Speaker 1: just a simple, uh, mathematical problem, you know, like, what's

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Speaker 1: what's two plus two? Well, there's an answer to that,

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Speaker 1: and it's four. Um an engineering problem. What's the how

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Speaker 1: do we build this thing so that it doesn't collapse?

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Speaker 1: There's an answer, it can achieved. You have a you

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Speaker 1: know what the mission is when you go into try

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Speaker 1: and solve it, and then you solve it. Um. We

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Speaker 1: love questions like that, and it's easy to look to

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Speaker 1: wicked problems and and try to solve them like that,

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Speaker 1: to want them to be solved like that. I've I've

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Speaker 1: read you know, that's one of the reasons that the

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Speaker 1: zombie um motif is so popular the zombie apocalypse, because

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Speaker 1: in the zombie apocalypse, all problems become tame or benign.

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Speaker 1: Zombie comes what do you do you shoot it in

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Speaker 1: the head, you can cut its head off, you kill it. Right,

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Speaker 1: those are relatively easy to solve problems, and it makes

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Speaker 1: the relevance of the wicked problems go away, right Yeah,

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Speaker 1: Like you know, your better zombie h fiction has wicked

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Speaker 1: problems in it as well. I feel like you look

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Speaker 1: at like Walking Dad, they're attempting to to to graft

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Speaker 1: in wicked problems into the narrative. But at heart, the

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Speaker 1: very sort of video game or Donna the Dead level,

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Speaker 1: it's all about tame benign problems. I was talking to

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Speaker 1: a friend about this yesterday as I was researching it

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Speaker 1: and saying, like, this is pretty fascinating stuff. You ever

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Speaker 1: heard of this? And he hadn't, But he said, uh,

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Speaker 1: well you know, and he may be like kind of

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Speaker 1: a broaden the scale of it, but he didn't look

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Speaker 1: at the research. He said, Well, life's a wicked problem,

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Speaker 1: isn't it. Like when you come down to it, the

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Speaker 1: human body is a wicked problem because no matter what

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Speaker 1: we do uh to the human body, no matter how

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Speaker 1: well we exercise, no matter what we eat that's healthy,

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Speaker 1: something's always gonna pop up that we can't control. Right, Yeah,

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Speaker 1: I mean that the self the mind is a wicked

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Speaker 1: problems problem. I think back to you know, the old

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Speaker 1: sound of music track. How do you solve a problem

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Speaker 1: like Maria? It's kind of a goofy reference point, but

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Speaker 1: how do you how do you solve a problem like Maria?

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Speaker 1: How do you solve a problem like like the individual,

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Speaker 1: like the self? Like that is a a complex situation,

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Speaker 1: is not just you know, an A plus B equals

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Speaker 1: see equation going on there. We spend our whole lives

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Speaker 1: trying to solve this unsolvable problem. Yeah, and so the

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Speaker 1: interesting thing out the wicked problem, I guess paradigm are

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Speaker 1: as a as a Slavo says, peredigma is is that

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Speaker 1: you know, that's the key to it is that is

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Speaker 1: learning to approach it that way and to say, okay,

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Speaker 1: well that's an that's unsolvable, right, That's not a thing

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Speaker 1: that can be fixed by its very nature. But there's

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Speaker 1: ways to mitigate it, there's ways to approach it differently,

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Speaker 1: and having that very position put you in a better position,

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Speaker 1: I guess to approach it. Right. So one major proposal

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Speaker 1: that keeps coming up, and in fact, uh, if you

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Speaker 1: google wicked problems, one of the first things that comes

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Speaker 1: up is a website for a book called Wicked Problems

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Speaker 1: that's by a design educational facility in Austin, Texas, and

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Speaker 1: the whole books available for free. Actually you can read

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Speaker 1: it online on the web where you can buy it

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Speaker 1: and print. But they basically say, look like, the way

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Speaker 1: to approach this is through strategic design, and it's a

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Speaker 1: combination of using empathy, abductive reasoning, and rapid prototyping. Those

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Speaker 1: are the ways that they sort of think about. You know,

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Speaker 1: let's let's approach these first of all acknowledge that they're

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Speaker 1: a wicked problem, but then you approach it afterwards. And so,

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Speaker 1: you know, just as a reminder, because I had to

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Speaker 1: remind myself. Abductive reasoning is that it is the opposite

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Speaker 1: of deductive logic. Right, where there's a premise that leads

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Speaker 1: to a conclusion with a solution, right, there's two premises

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Speaker 1: lead to a conclusion in staid, Abductive is that the

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Speaker 1: premise doesn't guarantee any solution, and in fact you have

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Speaker 1: to work from inference and it's the most simple solution

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Speaker 1: that's inferred that usually leads to some kind of uh,

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Speaker 1: not a solution in this case, betterment, I guess that

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Speaker 1: that makes sense in terms of what we're talking about here,

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Speaker 1: because one of the big problems is just even defining

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Speaker 1: what the problem is. You know, you look at something

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Speaker 1: like like poverty. Someone says, hey, we have a problem.

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Speaker 1: There's poverty, and you say, well, what is the problem?

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Speaker 1: Is it that people are poor because of the job situation?

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Speaker 1: Is is it more cultural? Uh? Is it? Does it

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Speaker 1: have to do with our laws? Does it have to

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Speaker 1: do with enforcement of said laws? You know that there's

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Speaker 1: some of one of those things like try to solve

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Speaker 1: the laws, and maybe it makes another thing worse, right, Like, um,

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Speaker 1: I'm trying to think of an example, but I keep

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Speaker 1: coming back to like the interconnectedness of hunger and poverty.

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Speaker 1: And then like I saw a really good in one

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Speaker 1: of the articles about wicked problems. It was a really

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Speaker 1: good example of why poverty is such a wicked problem.

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Speaker 1: Like we think of it like, oh, we'll solve that.

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Speaker 1: We've got this problem of poverty connected to people being hungry,

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Speaker 1: But at the same time, we've got a problem of

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Speaker 1: obesity in our society as well, And how are those

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Speaker 1: things connected? You know, Yeah, I've been thinking about this

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Speaker 1: food thing a lot recently, because I'm currently watching Michael

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Speaker 1: Pollan's latest documentary series on Is on Netflix, and it

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Speaker 1: has to do with cooking and where cooking comes from

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Speaker 1: and then how the industrialization of food preparation preparation has

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Speaker 1: changed everything. Um. So you you you see this situation

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Speaker 1: where like one side is trying to make things easier,

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Speaker 1: trying to correct problems, but that ends up creating other

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Speaker 1: problems as well. Yeah. See, so it's it's actually really

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Speaker 1: interesting how easily this can be applied. I go back

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Speaker 1: to when I was in grad school. I had a

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Speaker 1: professor who basically referred to stuff like this as like

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Speaker 1: the model fits type analysis. Right. So you've got this

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Speaker 1: model and you put it on top of something you're

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Speaker 1: like does it fit? Okay, but then you've got to

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Speaker 1: go beyond that and sort of synthesize them, you know what,

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Speaker 1: what you've learned from it, and analyze and go further. Um,

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Speaker 1: but let's start at the beginning. So where did this

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Speaker 1: idea come from? Like or the origin of wicked problems? Right? Like?

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Speaker 1: It just didn't it? Well, we've certainly had them forever.

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Speaker 1: But but in terms of thinking about that exactly. Yeah, Well,

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Speaker 1: the origination of the term is generally attributed to a

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Speaker 1: pair of Berkeley professors in the nineteen seventies, Horst W. J. Riddle,

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Speaker 1: Professor of the Science of Design the University of California,

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Speaker 1: Berkeley and Melvin M. Weber, Professor of City Planning, Berkeley. Uh.

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Speaker 1: And then occasionally you see people giving credit to philosopher

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Speaker 1: and system science and see West Churchman largely for popular

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Speaker 1: popularizing or modernizing it. But but basically it comes down

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Speaker 1: to Riddle and Webber in particular. Riddle and web Webber

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Speaker 1: really dove into the topic in the nine paper Dilemmas

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Speaker 1: in a General Theory of Planning, published a published in

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Speaker 1: Policy Sciences. Yeah, I uh for this episode. I went

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Speaker 1: through and read that, and there were certainly many things

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Speaker 1: that were relevant to the discussion we're gonna have today,

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Speaker 1: But it was so grounded in the American politics of

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Speaker 1: the early seventies that there's a lot of stuff that

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Speaker 1: was like WHOA, okay, but it was interesting too write

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Speaker 1: to be able to look back at. Well, it kind

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Speaker 1: of gets down to one of one of the things

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Speaker 1: that will discuss that they point out about wicked problems,

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Speaker 1: if it every wicked problem is different, to the point

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Speaker 1: that if you're talking about wicked problems like just in

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Speaker 1: the shadow of a particular area, if you're thinking if

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Speaker 1: you're talking about wicked problems generally, but really in the

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Speaker 1: back of the mind, we're thinking about a specific wicked

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Speaker 1: problem that colors your your definition of what a wicked

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Speaker 1: problem is. Now, I do want to read part of

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Speaker 1: a quote here from them where they really get into

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Speaker 1: the whole idea of why why they choose the word wicked,

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Speaker 1: which tends to inspire of evil yea or or Bostonian inflection.

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Speaker 1: They said that they show they referred to the problems

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Speaker 1: as wicked. Quote not because these properties are themselves ethically deplorable.

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Speaker 1: We use the term wicked in a meaning akin to

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Speaker 1: that of malignant, in a contrast to benign or vicious

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Speaker 1: like a circle, or tricky like a leprechn um. I

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Speaker 1: love that we're able to always bring back monsters into it. Yeah,

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Speaker 1: they and more of a it's a it's a fairy

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Speaker 1: folk with it's an unnatural creature, So I think it

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Speaker 1: counts um and they brought it up, not us says

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Speaker 1: that door tricky like a lepricn, or aggressive like a

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Speaker 1: lion in contrast to uh, you know a lamb, we

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Speaker 1: do not mean to personify these properties of social systems

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Speaker 1: by implying malicious intent. But then you may agree that

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Speaker 1: it becomes morally objectionable for the planner to treat a

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Speaker 1: wicked problem as though it were a tame one, or

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Speaker 1: to tame a wicked problem prematurely, or to refuse to

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Speaker 1: recognize the inherent wickedness of social problems. And so that

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Speaker 1: right there, that last bit is what I think gets

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Speaker 1: to the heart of what maybe the connection is today

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Speaker 1: is the refusal to recognize what this is right for

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Speaker 1: what it is. And that brings us back to that

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Speaker 1: political analogy of everything that's going on right now. Now,

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Speaker 1: whatever candidate you support or whatever candidate you don't support,

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Speaker 1: all of them are up there. That's the inherent nature

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Speaker 1: of the political system. Right when you're running for office,

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Speaker 1: you pretend like you have all the answers, uh, and

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Speaker 1: all of them are are are basically running on a

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Speaker 1: platform where they're like, Oh, that problem, I have the

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Speaker 1: answer to that. Yeah, that problem, I have the answer

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Speaker 1: to that. But for me, I'm anti hydra elect me

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Speaker 1: and I have don't worry, I have a plan. I'm

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Speaker 1: gonna bring some very classy people to exterminate that high

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Speaker 1: and it's it's worse than that, right, Like, you can't, man,

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Speaker 1: how refreshing would it be to have a candidate come

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Speaker 1: up and just be like, well, look like the problems

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Speaker 1: that we're facing are so chaotic and so complex that

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Speaker 1: we as human beings are just not equipped to be

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Speaker 1: able to solve all of them. Well, there's your problem.

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Speaker 1: That doesn't sound like a politician, that doesn't sound like somebody,

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Speaker 1: it's like it's like some kind of philosopher or something.

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Speaker 1: And we just shove that off in a corner and say, well,

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Speaker 1: that's that's not authoritative enough for what we need. Yeah,

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Speaker 1: actual contemplation of the wicked problems either comes after you're

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Speaker 1: elected or it falls to the people who are charged

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Speaker 1: with fixing things by the elected indivision. Yeah, that's absolutely true.

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Speaker 1: And the very idea that you can't formulate a definition

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Speaker 1: of what a wicked problem is is actually part of

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Speaker 1: what Riddle and Weber came up with as their ten characteristics.

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Speaker 1: So the bulk of their article was these characteristics that

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Speaker 1: they lay down, and they basically say, look, these are

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Speaker 1: not criteria a test to determine what the wickedness is,

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Speaker 1: but their insights to help you to decide if the

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Speaker 1: problem you're facing is wicked. So let's go through these briefly,

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Speaker 1: and I'll note for those about you out there counting,

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Speaker 1: there's actually eleven here in our list, and that's because

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Speaker 1: Riddle and Webber. I keep going to call him Horsed

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Speaker 1: because that's his first name. Riddle and Weber came up

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Speaker 1: with ten. But then over the years, as people have

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Speaker 1: written more about this and applied it to various things,

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Speaker 1: they've come up with their own and so they're basically

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Speaker 1: the same, but I tried to sort of merge them

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Speaker 1: together here for for the purpose of the podcast. So

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Speaker 1: I'll start with the first one, which is that wicked

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Speaker 1: problems have no definitive formulation. They are all different and

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Speaker 1: they can't contribute to solving one another in any complete way.

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Speaker 1: You can't write a well defined statement of these problems.

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Speaker 1: And this is a direct quote from the article by

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Speaker 1: Riddle and Webber. The process of formulating the problem and

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Speaker 1: of conceiving a solution are identical, since every specification of

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Speaker 1: the problem is a specification of the direction in which

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Speaker 1: a treatment is considered. Yeah, I think one example that

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Speaker 1: comes to mind. Here is the war on drugs, right,

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Speaker 1: like epidemic is a problem, and then it falls to heaven,

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Speaker 1: how do you define the problem and then go after

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Speaker 1: it if you end up approaching it from a purely

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Speaker 1: you know, law enforcement, right when you brand it as

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Speaker 1: a war and you have chosen the direction and uh yeah,

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Speaker 1: and then it's easy for us to look back now,

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Speaker 1: look back at the eighties now and go, oh, why

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Speaker 1: did we brand it as a war on drugs? Right?

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Speaker 1: But at the time it seemed like a solution to

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Speaker 1: a problem to them. Yeah, yeah, I mean hindsight twenty

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Speaker 1: on that. But then but then once you've employed that strategy,

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Speaker 1: you have changed the problems we'll discuss. So number two.

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Speaker 1: Wicked problems involve many stakeholders, all of whom have different

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Speaker 1: ideas about what the problem is and what its causes are.

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Speaker 1: This again, in think of any any portion of the

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Speaker 1: world where there's a lot of conflict, like my mind

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Speaker 1: instantly goes to, uh, at least a couple of different

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Speaker 1: corners in the Middle East. I think of Israel and Palestine.

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Speaker 1: I think of the current situation in Syria. You have

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Speaker 1: different stakeholders that are involved at different levels who who

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Speaker 1: want different things out of this, but they want them

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Speaker 1: in the name of solving the amorphous problem here totally

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Speaker 1: and at the micro level, I think we're all familiar with,

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Speaker 1: you know, being in a kind of work situation or

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Speaker 1: involved in any organization. Maybe it's not work, maybe it's

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Speaker 1: your I don't know, you're you're housing organization that governs

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Speaker 1: the apartment complex you you're in. But you have all

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Speaker 1: these different stakeholders. Everybody's got their own subjective position on

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Speaker 1: these things, right, and they all have different goals too,

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Speaker 1: um So that ultimately, even just recognizing that goes a

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Speaker 1: long way towards making things a little bit better. All right,

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Speaker 1: this is number three. It might be impossible to measure

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Speaker 1: any kind of success with the wicked problem given their interconnectedness.

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Speaker 1: The search for solutions will never stop. There's a very

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Speaker 1: hydra portion of the argument here, because any any time

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Speaker 1: you you actually try and solve a complex problem, you

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Speaker 1: have to what extent is your solution creating new problems,

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Speaker 1: um and then not addressing other areas that are all

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Speaker 1: a part of the same issue. So, for instance, poverty,

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Speaker 1: if you're just if you're just trying to solve the problem,

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Speaker 1: the wicked problem of poverty by looking at jobs. That's

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Speaker 1: that's gonna that's gonna help some people. That is gonna

427
00:23:28,440 --> 00:23:31,520
Speaker 1: help everyone, is gonna erase poverty. No, okay, the fourth one.

428
00:23:31,800 --> 00:23:34,800
Speaker 1: There are no true or false solutions to wicked problems,

429
00:23:34,920 --> 00:23:39,119
Speaker 1: only good or bad subject It's all subjective, right, um,

430
00:23:39,200 --> 00:23:43,000
Speaker 1: And everyone's judgments will differ, and the solutions can only

431
00:23:43,040 --> 00:23:46,439
Speaker 1: be described as in that good bad paradigm or and

432
00:23:46,520 --> 00:23:49,120
Speaker 1: this is from the Riddle and Webber thing, but what's

433
00:23:49,160 --> 00:23:53,120
Speaker 1: probably better to describe it as is better or worse? Right,

434
00:23:53,480 --> 00:23:56,480
Speaker 1: things got better or things got worse, not they're good

435
00:23:56,480 --> 00:23:59,480
Speaker 1: now they're bad now? Yeah, Like I I think back

436
00:23:59,480 --> 00:24:02,040
Speaker 1: to the War on drugs. Like you can imagine someone saying, hey,

437
00:24:02,640 --> 00:24:05,040
Speaker 1: we applied this solution to the wicked problem. And then

438
00:24:05,040 --> 00:24:07,240
Speaker 1: someone says, well, you put a lot of these people

439
00:24:07,280 --> 00:24:09,480
Speaker 1: had drugs and they're in prison. Now they're off the streets.

440
00:24:09,520 --> 00:24:12,679
Speaker 1: That's good, right. And then someone might say, well, it

441
00:24:12,800 --> 00:24:17,120
Speaker 1: also means that our prison population is extremely overloaded with

442
00:24:17,160 --> 00:24:20,320
Speaker 1: these low level offenders. That's bad. They say, that's bad, right,

443
00:24:20,359 --> 00:24:22,880
Speaker 1: And the two things are like it's like a scale.

444
00:24:23,160 --> 00:24:25,280
Speaker 1: It's like this like situation where you've got all these

445
00:24:25,320 --> 00:24:28,280
Speaker 1: different scales that are attached to one another, and anytime

446
00:24:28,320 --> 00:24:32,040
Speaker 1: you move one little thing, everything shifts a little bit. Yeah,

447
00:24:32,080 --> 00:24:34,480
Speaker 1: it's like seating on an airplane. It's like, all right,

448
00:24:34,920 --> 00:24:38,120
Speaker 1: they move their chair back, that's bad. I move mind back,

449
00:24:38,160 --> 00:24:40,640
Speaker 1: that's good. But now the person behind me is uncomfortable,

450
00:24:40,680 --> 00:24:44,200
Speaker 1: and now the person beside me, and it gets everything

451
00:24:44,240 --> 00:24:46,560
Speaker 1: gets out of whack, and everybody's miserable and there's nothing

452
00:24:46,600 --> 00:24:50,919
Speaker 1: you can do about it. The domino effective misery. Yeah, alright.

453
00:24:51,200 --> 00:24:54,600
Speaker 1: Number five, there's no template to follow when tackling a

454
00:24:54,640 --> 00:24:57,440
Speaker 1: wicked problem. There's no way to determine right away if

455
00:24:57,480 --> 00:25:02,160
Speaker 1: a if A solution is working. So yeah, this gets

456
00:25:02,160 --> 00:25:05,040
Speaker 1: into just the problem of this is where we come

457
00:25:05,040 --> 00:25:07,040
Speaker 1: back to the example you mentioned earlier where they mentioned

458
00:25:07,080 --> 00:25:10,520
Speaker 1: rapid prototyping, which I guess would work in with certain

459
00:25:10,600 --> 00:25:15,440
Speaker 1: types of wicked problems, but certainly larger issues out there,

460
00:25:15,480 --> 00:25:19,960
Speaker 1: like how do you rapid prototype towards you know, dealing

461
00:25:20,000 --> 00:25:22,680
Speaker 1: with crime, or dealing with poverty, or dealing with hunger,

462
00:25:22,880 --> 00:25:26,000
Speaker 1: or or or or any number of what we could

463
00:25:26,000 --> 00:25:28,520
Speaker 1: problems that pop up. It's especially on that scale it's

464
00:25:28,600 --> 00:25:32,240
Speaker 1: especially difficult. Uh, we'll talk a little bit more about

465
00:25:32,280 --> 00:25:34,360
Speaker 1: I think what they meant by rapid prototyping, but it's

466
00:25:34,400 --> 00:25:37,920
Speaker 1: essentially uh, the gist is that, like, rather than come

467
00:25:38,000 --> 00:25:40,960
Speaker 1: up with one solution to approach a problem with and

468
00:25:40,960 --> 00:25:43,040
Speaker 1: then see if that works, and then if it doesn't,

469
00:25:43,080 --> 00:25:45,000
Speaker 1: then come up with another solution and keep trying them

470
00:25:45,000 --> 00:25:48,320
Speaker 1: over and over again, they recommend coming up with multiple

471
00:25:48,359 --> 00:25:51,959
Speaker 1: solutions and trying them all at once. But you know

472
00:25:52,080 --> 00:25:57,200
Speaker 1: there's problems with that too, so not rapid in succession. Yeah,

473
00:25:57,240 --> 00:26:00,399
Speaker 1: I believe they were like scatter shot exactly. That's the

474
00:26:00,440 --> 00:26:08,440
Speaker 1: shotgun method like that, Okay. Uh. The number six is

475
00:26:08,480 --> 00:26:12,000
Speaker 1: there's always more than one explanation for a wicked problem,

476
00:26:12,119 --> 00:26:14,000
Speaker 1: and you can see that inherent in the examples that

477
00:26:14,000 --> 00:26:17,040
Speaker 1: we've just mentioned as well. Yeah, number seven, Every wicked

478
00:26:17,040 --> 00:26:20,040
Speaker 1: problem is a symptom of another wicked problem and there's

479
00:26:20,080 --> 00:26:23,679
Speaker 1: no single root cause. So back to the interconnectedness and

480
00:26:23,800 --> 00:26:27,080
Speaker 1: the hydra nature of it, right, And this one, this one,

481
00:26:27,160 --> 00:26:29,520
Speaker 1: I think is one of the most important of their

482
00:26:30,040 --> 00:26:33,320
Speaker 1: their ten characteristics here, specifically for us here it's stuff

483
00:26:33,359 --> 00:26:35,399
Speaker 1: to blow your mind. They say there's no way to

484
00:26:35,520 --> 00:26:41,600
Speaker 1: scientifically test wicked problem strategies because they're all human inventions,

485
00:26:41,640 --> 00:26:44,400
Speaker 1: they are outside of nature. Right, So when we're thinking

486
00:26:44,440 --> 00:26:47,959
Speaker 1: about all these problems, like, let's go back to the hydra, right, Like,

487
00:26:48,160 --> 00:26:50,600
Speaker 1: the hydra is a natural being that we are learning

488
00:26:50,640 --> 00:26:52,920
Speaker 1: to understand by looking at through that. We're talking about

489
00:26:52,920 --> 00:26:57,200
Speaker 1: the biological biological hydra, right, and we now understand how

490
00:26:57,240 --> 00:27:00,480
Speaker 1: it's mouth opens because we looked very close slee at

491
00:27:00,520 --> 00:27:07,440
Speaker 1: it with uh light microscope. But poverty is a human invention.

492
00:27:08,400 --> 00:27:11,320
Speaker 1: Uh so, so how do we look at that with

493
00:27:11,320 --> 00:27:13,920
Speaker 1: a microscope? Yeah, I mean the mythological hydra is of

494
00:27:14,000 --> 00:27:17,440
Speaker 1: human creation changes every time you tell it exactly. Yeah,

495
00:27:17,520 --> 00:27:19,679
Speaker 1: you can't. So many of these problems you can't just

496
00:27:19,760 --> 00:27:22,359
Speaker 1: apply physics, and so you can't just apply look at

497
00:27:22,400 --> 00:27:25,040
Speaker 1: it from a fluid dynamics standpoint and try and figure

498
00:27:25,080 --> 00:27:27,520
Speaker 1: it out. Maybe that can be helpful in some cases

499
00:27:27,560 --> 00:27:31,840
Speaker 1: in figuring out a part of the problem if it's applicable,

500
00:27:31,880 --> 00:27:36,639
Speaker 1: but but probably not. Number nine. Solutions to wicked problems

501
00:27:36,640 --> 00:27:40,240
Speaker 1: are usually one shot efforts that minimize trial and error efforts.

502
00:27:40,280 --> 00:27:43,919
Speaker 1: Every implemented solution has quant consequences that cannot be undone

503
00:27:43,960 --> 00:27:47,720
Speaker 1: and this is where we get to um the fact

504
00:27:47,720 --> 00:27:50,159
Speaker 1: that every time you try and solve the wicked problem,

505
00:27:50,240 --> 00:27:52,959
Speaker 1: you change the problem, and now you have a slightly

506
00:27:52,960 --> 00:27:55,800
Speaker 1: different problem you have to deal with. It's not like

507
00:27:55,840 --> 00:27:58,520
Speaker 1: a mathematical equation where you like you figure out what

508
00:27:58,840 --> 00:28:01,919
Speaker 1: X is right like every time in these situations, if

509
00:28:01,960 --> 00:28:04,080
Speaker 1: you figure out what X is, then like it changes

510
00:28:04,080 --> 00:28:06,640
Speaker 1: what the definition of all the other numbers are the

511
00:28:06,680 --> 00:28:09,359
Speaker 1: original equation. It's kind of like this this Rubik's cute

512
00:28:09,440 --> 00:28:11,800
Speaker 1: that is on the table in the podcast chamber we're

513
00:28:11,800 --> 00:28:14,359
Speaker 1: we're recording right now. It's like if I try, I'm

514
00:28:14,359 --> 00:28:17,400
Speaker 1: trying to solve this thing, but every time I move it,

515
00:28:17,560 --> 00:28:20,159
Speaker 1: I cannot move it back to where it was. And

516
00:28:20,200 --> 00:28:23,520
Speaker 1: I'm just I'm just lost every time because each time

517
00:28:23,640 --> 00:28:26,240
Speaker 1: I try to solve it, it is a new problem

518
00:28:26,480 --> 00:28:29,119
Speaker 1: that I never get a second shot. It's solving the

519
00:28:29,200 --> 00:28:32,840
Speaker 1: same problem. The rubikscube is a great metaphor for wicked problems.

520
00:28:32,880 --> 00:28:42,240
Speaker 1: That would be maybe the Lament configuration. Okay, every wicked

521
00:28:42,280 --> 00:28:45,600
Speaker 1: problem is unique. This is number ten. There is no precedent.

522
00:28:45,720 --> 00:28:48,400
Speaker 1: So what this means is essentially that you can't look

523
00:28:48,440 --> 00:28:51,720
Speaker 1: back to any previous wicked problem that you've tried to

524
00:28:51,760 --> 00:28:54,800
Speaker 1: make better as like a template to say Okay, well,

525
00:28:54,880 --> 00:28:56,720
Speaker 1: let's try the same thing that we tried with that

526
00:28:56,960 --> 00:28:59,120
Speaker 1: here and see if it works. Because they're so totally

527
00:28:59,240 --> 00:29:03,280
Speaker 1: unique that there's no there's no model to work from.

528
00:29:03,320 --> 00:29:06,360
Speaker 1: At number eleven, this is an interesting one. Designers attempting

529
00:29:06,360 --> 00:29:09,520
Speaker 1: to address a wicked problem must be fully responsible for

530
00:29:09,560 --> 00:29:13,880
Speaker 1: their actions. Yeah, so this one, Um, I don't know

531
00:29:13,880 --> 00:29:16,240
Speaker 1: that I had trouble with it as much as just that, like,

532
00:29:16,520 --> 00:29:19,720
Speaker 1: you know, it's essentially a mission statement by the authors

533
00:29:19,760 --> 00:29:22,480
Speaker 1: here saying like, okay, so if you're going to approach

534
00:29:22,560 --> 00:29:28,480
Speaker 1: this from a design policy standpoint, you have to own it. Yeah,

535
00:29:28,840 --> 00:29:32,080
Speaker 1: and certainly, I mean this seems like it it is,

536
00:29:32,200 --> 00:29:34,600
Speaker 1: or at least certainly should be just part of the

537
00:29:35,200 --> 00:29:39,120
Speaker 1: you know, any political attempt or military attempt or what

538
00:29:39,200 --> 00:29:43,959
Speaker 1: have you to to tackle any kind of socioeconomic wicked problem.

539
00:29:44,040 --> 00:29:47,080
Speaker 1: Is that. Yeah, anything you do, you should be held accountable.

540
00:29:47,800 --> 00:29:52,400
Speaker 1: But as we often see that accountability here doesn't always

541
00:29:52,880 --> 00:29:55,760
Speaker 1: spread to everyone in the scenario. It's also important to

542
00:29:55,840 --> 00:29:58,440
Speaker 1: note here, like we can say though, that not all

543
00:29:58,600 --> 00:30:01,880
Speaker 1: hard to solve problem is are wicked. Only those that

544
00:30:02,000 --> 00:30:04,160
Speaker 1: have an indeterminate scope and scale. So let's go back

545
00:30:04,160 --> 00:30:07,040
Speaker 1: to the Rubik's cube. Right, that's a hard to solve problem,

546
00:30:07,400 --> 00:30:10,560
Speaker 1: but it cannot. Yeah, well, you have a clear objective

547
00:30:10,640 --> 00:30:12,680
Speaker 1: to like, how do you solve this thing? Will you

548
00:30:12,720 --> 00:30:14,960
Speaker 1: get I'll know it's solved when I have all the

549
00:30:15,000 --> 00:30:17,840
Speaker 1: same colors on the same side. So, like wicked problems,

550
00:30:18,160 --> 00:30:20,760
Speaker 1: you don't know at what point do you know it's solved.

551
00:30:21,440 --> 00:30:24,360
Speaker 1: And there's also no instant feedback because the effects of

552
00:30:24,360 --> 00:30:29,920
Speaker 1: trying to to to implement changes, say you know in society, Um,

553
00:30:29,960 --> 00:30:32,360
Speaker 1: the you're not gonna get instant to feedback. You're gonna

554
00:30:32,360 --> 00:30:36,280
Speaker 1: get feedback rolling in in waves over years, decades to come,

555
00:30:36,520 --> 00:30:38,520
Speaker 1: like like we were just mentioning with the war on drugs,

556
00:30:38,720 --> 00:30:41,200
Speaker 1: it's a lot easier for us now, uh, you know,

557
00:30:41,440 --> 00:30:44,560
Speaker 1: thirty plus years later to say that seems like a

558
00:30:44,600 --> 00:30:46,680
Speaker 1: silly approach, or at least the branding approach to it.

559
00:30:46,720 --> 00:30:50,080
Speaker 1: I don't want to criticize the methodology necessarily. All Right,

560
00:30:50,280 --> 00:30:52,840
Speaker 1: we're gonna take a quick break and when we come back,

561
00:30:53,040 --> 00:30:56,960
Speaker 1: we will discuss macro wicked problems, wicked problems in science,

562
00:30:56,960 --> 00:31:08,360
Speaker 1: and micro wicked problems. All right, we're back. So we've

563
00:31:08,360 --> 00:31:12,280
Speaker 1: been talking about wicked problems and how that it's a

564
00:31:12,320 --> 00:31:15,760
Speaker 1: framework that we can sort of apply almost to anything. Right, Like,

565
00:31:15,880 --> 00:31:17,560
Speaker 1: as I was saying before, I had a friend who

566
00:31:17,600 --> 00:31:19,160
Speaker 1: was like, well, you can talk about the human body

567
00:31:19,160 --> 00:31:22,360
Speaker 1: as being a wicked problem, but um, let's take a

568
00:31:22,360 --> 00:31:25,600
Speaker 1: look at sort of the definition that it was originally

569
00:31:25,640 --> 00:31:28,520
Speaker 1: assigned for. And what I'm calling for the purposes of

570
00:31:28,520 --> 00:31:33,520
Speaker 1: this episode macro wicked problems, which are the things The

571
00:31:33,560 --> 00:31:35,400
Speaker 1: best example I can think of to tie this to

572
00:31:36,000 --> 00:31:39,200
Speaker 1: is the current political campaign, right, So uh, and maybe

573
00:31:39,240 --> 00:31:41,920
Speaker 1: you're not American, but you're probably familiar with all the

574
00:31:42,000 --> 00:31:44,640
Speaker 1: zan nous that's going on in our political process right now,

575
00:31:44,920 --> 00:31:48,120
Speaker 1: or maybe wherever you're from, you I can't imagine that

576
00:31:48,200 --> 00:31:51,120
Speaker 1: politics are all that much different. It's the same friend

577
00:31:51,440 --> 00:31:54,520
Speaker 1: who mentioned the human body thing had recently traveled to

578
00:31:54,600 --> 00:31:57,120
Speaker 1: Ghana and he was like, yeah, you know, over there,

579
00:31:57,320 --> 00:31:59,600
Speaker 1: there was an election cycle in process while I was

580
00:31:59,680 --> 00:32:03,120
Speaker 1: visiting there, and it was the same as it is here.

581
00:32:03,280 --> 00:32:06,520
Speaker 1: It's just on a different level. So what we're talking

582
00:32:06,560 --> 00:32:09,480
Speaker 1: about here are the stances of where these you know,

583
00:32:09,520 --> 00:32:12,920
Speaker 1: these various politicians, I think that they have the one

584
00:32:13,040 --> 00:32:16,760
Speaker 1: single answer to solve a problem that society is basically

585
00:32:16,800 --> 00:32:20,240
Speaker 1: you know, arguing over right, the budget and economy, civil rights,

586
00:32:21,160 --> 00:32:26,240
Speaker 1: rights of corporations, uh, crime, drugs, we've mentioned those already. Education,

587
00:32:26,880 --> 00:32:32,680
Speaker 1: how we use energy and oil, the environment, uh, foreign policy,

588
00:32:33,360 --> 00:32:37,360
Speaker 1: free trade, how we reform our government. That one alone, Wow,

589
00:32:37,400 --> 00:32:39,720
Speaker 1: what a tangled mess that would be. Yeah, I mean,

590
00:32:40,560 --> 00:32:43,120
Speaker 1: did you mention climate change and the climate changes at

591
00:32:43,120 --> 00:32:46,000
Speaker 1: the end here? Yeah, that's a big one, because I

592
00:32:46,040 --> 00:32:49,160
Speaker 1: mean that one, it's all the definitions are talking about

593
00:32:49,480 --> 00:32:52,520
Speaker 1: multiple stakeholders. Yeah, I mean that one. That one fits

594
00:32:52,600 --> 00:32:55,760
Speaker 1: most of the criteria we've been discussing, especially multiple stakeholders,

595
00:32:55,800 --> 00:32:59,480
Speaker 1: different views on what what success means and what the

596
00:32:59,560 --> 00:33:01,800
Speaker 1: root cause is and what the problem is to begin with,

597
00:33:02,760 --> 00:33:05,760
Speaker 1: gun control, same thing, right, It's such a complex issue.

598
00:33:05,800 --> 00:33:08,680
Speaker 1: It's not just you know, it's always interesting to try

599
00:33:08,680 --> 00:33:10,400
Speaker 1: to bring it down to a personal level. But you know,

600
00:33:10,480 --> 00:33:13,480
Speaker 1: like I've I have friends who own guns and love

601
00:33:13,520 --> 00:33:17,080
Speaker 1: their guns and are very uh very much wants to

602
00:33:17,160 --> 00:33:20,280
Speaker 1: keep their guns and are against gun control. And it's

603
00:33:20,320 --> 00:33:23,960
Speaker 1: not for them. It's it's not like a connected to

604
00:33:24,000 --> 00:33:26,480
Speaker 1: crime at all. Right, they don't see that. But then

605
00:33:26,520 --> 00:33:29,720
Speaker 1: there's the wicked problem of the connections between gun control

606
00:33:30,200 --> 00:33:33,280
Speaker 1: and crime and drugs and education. You see, you see

607
00:33:33,280 --> 00:33:35,200
Speaker 1: how they they all kind of span together. Oh yeah,

608
00:33:35,200 --> 00:33:37,480
Speaker 1: I mean on the gun control issue. And we see

609
00:33:37,520 --> 00:33:39,560
Speaker 1: it time and time again, Like the issue comes up

610
00:33:39,960 --> 00:33:43,200
Speaker 1: and you know, one side says, oh, what's it's not

611
00:33:43,240 --> 00:33:45,440
Speaker 1: a it's not a gun issue, it's a mental health issue.

612
00:33:45,480 --> 00:33:47,920
Speaker 1: I just say, well, if you take all the guns away,

613
00:33:47,960 --> 00:33:50,480
Speaker 1: you're still not people are still going to kill each other.

614
00:33:50,560 --> 00:33:53,520
Speaker 1: I mean it goes back and forth with people. Uh

615
00:33:53,720 --> 00:33:56,200
Speaker 1: people are arguing, and that the one thing that's becoming

616
00:33:56,200 --> 00:33:59,360
Speaker 1: clear is that we don't even have a full graphs.

617
00:33:59,440 --> 00:34:01,840
Speaker 1: Like the problem it's self is as a morphous and

618
00:34:01,840 --> 00:34:05,120
Speaker 1: and just and shapeless, and all that these different voices

619
00:34:05,160 --> 00:34:07,480
Speaker 1: are doing are all they're doing is trying to give

620
00:34:07,520 --> 00:34:11,480
Speaker 1: shape to the problem. Yeah, exactly when you can't. Uh So,

621
00:34:11,560 --> 00:34:13,480
Speaker 1: a couple of really quick other ones, right that you're

622
00:34:13,520 --> 00:34:16,120
Speaker 1: going to be hearing about, or you're probably currently hearing about,

623
00:34:16,120 --> 00:34:20,560
Speaker 1: homeland security, immigration, that's a big one, Jobs, social security,

624
00:34:20,600 --> 00:34:25,879
Speaker 1: tax reform. Technology. Just just like this kept coming back

625
00:34:25,920 --> 00:34:28,400
Speaker 1: to me because we work in the digital media industry

626
00:34:28,600 --> 00:34:32,120
Speaker 1: and it changes so quickly that it's interesting to see

627
00:34:32,160 --> 00:34:38,000
Speaker 1: how both policymakers and business people try to adapt with it.

628
00:34:38,080 --> 00:34:41,640
Speaker 1: And it's it's it's impossible to sort of predict the

629
00:34:41,680 --> 00:34:45,719
Speaker 1: wave of how it's gonna progress, right, Um, and then

630
00:34:45,760 --> 00:34:48,480
Speaker 1: there's of course the good old war and poverty ones.

631
00:34:48,920 --> 00:34:52,719
Speaker 1: So yeah, these are all huge issues that affect all

632
00:34:52,800 --> 00:34:55,239
Speaker 1: of us. And I think like, based on the definition

633
00:34:55,600 --> 00:34:58,080
Speaker 1: that we set up for you before the break, you

634
00:34:58,120 --> 00:35:01,200
Speaker 1: can see how these are all wicked problems. All right,

635
00:35:01,239 --> 00:35:04,720
Speaker 1: we've already discussed with the problem of trying to apply

636
00:35:05,440 --> 00:35:09,200
Speaker 1: science to wicked problems that it's not uh an A

637
00:35:09,280 --> 00:35:13,200
Speaker 1: plus B equalcy scenario. It's not like saying, oh, how

638
00:35:13,200 --> 00:35:14,959
Speaker 1: do we get people to the moon? That's a hard

639
00:35:15,000 --> 00:35:18,279
Speaker 1: problem to solve. We solved it because we knew what

640
00:35:18,360 --> 00:35:21,560
Speaker 1: the problem was, and we knew what the destination was,

641
00:35:21,719 --> 00:35:25,560
Speaker 1: and we knew how to know that we had succeeded. Yeah,

642
00:35:25,600 --> 00:35:29,160
Speaker 1: that's absolutely true. And so the this is actually the

643
00:35:29,200 --> 00:35:32,480
Speaker 1: impetus for us talking about wicked problems here today is

644
00:35:32,520 --> 00:35:35,440
Speaker 1: there was a great article earlier in the week that

645
00:35:35,480 --> 00:35:37,920
Speaker 1: came out from the bangor Daily News, and it was

646
00:35:37,960 --> 00:35:41,000
Speaker 1: written by a woman named Linda Silka, and she is

647
00:35:41,040 --> 00:35:44,320
Speaker 1: a social and community psychologist at the University of Maine,

648
00:35:44,760 --> 00:35:49,080
Speaker 1: and basically she was addressing how Maine as a state

649
00:35:49,360 --> 00:35:53,160
Speaker 1: is coming together and trying to approach their wicked problems

650
00:35:53,200 --> 00:35:58,600
Speaker 1: in a different way, uh, using science basically, And she says,

651
00:35:59,360 --> 00:36:01,080
Speaker 1: and this is this is the key here, This is

652
00:36:01,080 --> 00:36:03,239
Speaker 1: why I thought this really connected well to our show.

653
00:36:03,560 --> 00:36:07,040
Speaker 1: She argues, Look, there's this popular culture image of science

654
00:36:07,360 --> 00:36:10,640
Speaker 1: where there's a lab coded researcher and they prove a

655
00:36:10,800 --> 00:36:15,359
Speaker 1: brilliant idea and h then you know they've solved it, right,

656
00:36:15,400 --> 00:36:20,200
Speaker 1: They've solved a problem, or they've given humanity more knowledge

657
00:36:20,239 --> 00:36:23,840
Speaker 1: about a thing the hydra. Right we we when we

658
00:36:24,000 --> 00:36:29,560
Speaker 1: discussed the actual hydra and its anatomy in the previous episode,

659
00:36:29,840 --> 00:36:32,319
Speaker 1: the way that we talked about it, I had that

660
00:36:32,400 --> 00:36:34,960
Speaker 1: in my head. I'm I'm imagining that some of our

661
00:36:35,000 --> 00:36:37,680
Speaker 1: listeners did, too, of people in lab coats looking at

662
00:36:37,719 --> 00:36:42,200
Speaker 1: hydras under microscopes and going, ah, we solved it right. Yeah.

663
00:36:42,400 --> 00:36:46,320
Speaker 1: And you know, we encountered this occasionally with with listeners

664
00:36:46,320 --> 00:36:49,680
Speaker 1: and readers stuff about your mind, because there's that vision.

665
00:36:49,719 --> 00:36:53,640
Speaker 1: But but science also involves getting it wrong, getting a

666
00:36:53,640 --> 00:36:56,080
Speaker 1: wrong a lot like. That's how we helped define what

667
00:36:56,120 --> 00:36:59,480
Speaker 1: we know and what direction research is going by making mistakes,

668
00:37:00,040 --> 00:37:03,880
Speaker 1: mistakes that you you can you can't necessarily make in

669
00:37:04,000 --> 00:37:08,400
Speaker 1: tackling a wicked problem. Yeah. I mean so like for

670
00:37:08,480 --> 00:37:10,120
Speaker 1: some of the things that we work on here at

671
00:37:10,120 --> 00:37:12,680
Speaker 1: how stuff works, this comes up again and again where

672
00:37:13,400 --> 00:37:19,080
Speaker 1: we're tasked with answering how X works, right, and there

673
00:37:19,320 --> 00:37:24,680
Speaker 1: isn't always an answer it the uh very often, especially

674
00:37:24,680 --> 00:37:27,200
Speaker 1: like when Joe and I are writing for our general

675
00:37:27,200 --> 00:37:30,760
Speaker 1: science video show brain Stuff. The answer is, well, science

676
00:37:30,800 --> 00:37:34,120
Speaker 1: doesn't know yet, but here's what we do know, right,

677
00:37:34,440 --> 00:37:36,960
Speaker 1: and there. I can't tell you how many times we've

678
00:37:36,960 --> 00:37:40,000
Speaker 1: seen answers in the comments on social media or YouTube

679
00:37:40,080 --> 00:37:41,839
Speaker 1: or something where people are like, well, why did you

680
00:37:41,880 --> 00:37:45,880
Speaker 1: even bother to make this because science doesn't have the answer,

681
00:37:46,239 --> 00:37:49,400
Speaker 1: And I think, well, the the the importance is like, well,

682
00:37:49,640 --> 00:37:52,160
Speaker 1: when we cover what we do know, then we're able

683
00:37:52,160 --> 00:37:54,520
Speaker 1: to sort of approach it differently, right, Yeah, I mean

684
00:37:54,520 --> 00:37:56,879
Speaker 1: it kind of gets down into that area too of

685
00:37:57,200 --> 00:37:59,400
Speaker 1: this is something science doesn't know, but here are some

686
00:37:59,520 --> 00:38:03,719
Speaker 1: theories is to how it might work. That's just that's

687
00:38:03,719 --> 00:38:08,680
Speaker 1: is how we feel our way out exactly forward. Um. So,

688
00:38:08,760 --> 00:38:12,160
Speaker 1: Silko's argument is that this lab coded research myth is

689
00:38:12,320 --> 00:38:15,680
Speaker 1: becoming outdated and that we need to make efforts to

690
00:38:15,960 --> 00:38:21,040
Speaker 1: ensure that research can help solve the societal challenges we have,

691
00:38:21,480 --> 00:38:23,520
Speaker 1: like wicked problems, like all the things that we were

692
00:38:23,560 --> 00:38:26,000
Speaker 1: just defining. But she's looking at it very much from

693
00:38:26,000 --> 00:38:28,720
Speaker 1: a main perspective because she's working at the University of Maine.

694
00:38:29,120 --> 00:38:31,399
Speaker 1: So she says, in order to solve these science needs

695
00:38:31,400 --> 00:38:34,239
Speaker 1: to be approached in a more complex way. There needs

696
00:38:34,280 --> 00:38:39,239
Speaker 1: to be interaction between scientists, decision makers and citizens. And

697
00:38:39,280 --> 00:38:41,719
Speaker 1: you may have heard about this discussed elsewhere. Some people

698
00:38:41,800 --> 00:38:44,719
Speaker 1: label it as citizens science. I know a couple of

699
00:38:44,760 --> 00:38:46,600
Speaker 1: years ago I went to south By Southwest and that

700
00:38:46,680 --> 00:38:48,560
Speaker 1: was like the big term that was being thrown around.

701
00:38:48,560 --> 00:38:51,640
Speaker 1: A lot of people were creating apps that allowed everybody

702
00:38:51,640 --> 00:38:53,520
Speaker 1: to be a citizen scientist and to go out and

703
00:38:53,520 --> 00:38:56,680
Speaker 1: gather data with their their phones by like taking pictures

704
00:38:56,680 --> 00:38:59,800
Speaker 1: and then it would upload to a particular scientists laboratory

705
00:39:00,000 --> 00:39:02,600
Speaker 1: have various efforts to say everybody, like everybody take pictures

706
00:39:02,640 --> 00:39:05,400
Speaker 1: of whale sharks the encounter and looking to a database.

707
00:39:05,760 --> 00:39:08,960
Speaker 1: Everyone used a screen saver that enables Setty to search

708
00:39:09,000 --> 00:39:11,799
Speaker 1: for intelligent lot. The library that I used to work

709
00:39:11,840 --> 00:39:15,399
Speaker 1: at was part of the World Community Grid that contributed

710
00:39:15,480 --> 00:39:19,279
Speaker 1: basically whenever the computers were uh in sleep mode, they

711
00:39:19,280 --> 00:39:25,480
Speaker 1: were contributing their processing power towards solving scientific problems. Uh

712
00:39:25,520 --> 00:39:28,880
Speaker 1: so yeah. So her argument is essentially that all of

713
00:39:28,960 --> 00:39:31,640
Speaker 1: us need to know about the issues, and all of

714
00:39:31,719 --> 00:39:34,080
Speaker 1: us need to be involved because we all have important

715
00:39:34,200 --> 00:39:37,439
Speaker 1: roles to play. And she says, let's move away from

716
00:39:37,480 --> 00:39:40,640
Speaker 1: what is called the loading doc approach to science, and

717
00:39:40,680 --> 00:39:43,480
Speaker 1: the metaphor for that loading doc approach to science goes

718
00:39:43,520 --> 00:39:46,760
Speaker 1: like this. You've got a scientist and they're basically acting

719
00:39:46,840 --> 00:39:50,319
Speaker 1: like a factory that produces widgets, right, and they're not

720
00:39:50,360 --> 00:39:53,239
Speaker 1: producing widgets for any particular person. Then they just put

721
00:39:53,280 --> 00:39:55,560
Speaker 1: those widgets on the loading dock and they wait for

722
00:39:55,600 --> 00:39:58,040
Speaker 1: somebody to come along and go, oh, that's something I

723
00:39:58,080 --> 00:40:00,719
Speaker 1: have a use for and take it away right. Um,

724
00:40:00,800 --> 00:40:04,080
Speaker 1: And I see this, you know, having worked in academia previously.

725
00:40:04,400 --> 00:40:07,200
Speaker 1: Of course, like the way that that system is set up. Uh,

726
00:40:07,239 --> 00:40:10,480
Speaker 1: it's not always necessarily working conjunction to solve a problem.

727
00:40:10,640 --> 00:40:12,520
Speaker 1: More often it's kind of like I need to get

728
00:40:12,520 --> 00:40:17,040
Speaker 1: published that I can get right. Uh So, instead of

729
00:40:17,040 --> 00:40:20,319
Speaker 1: the loading doc procedure, she says, we should create a

730
00:40:20,400 --> 00:40:23,640
Speaker 1: product that is useful to people who actually need it.

731
00:40:23,680 --> 00:40:26,960
Speaker 1: So science should try to work together to figure out

732
00:40:27,040 --> 00:40:29,439
Speaker 1: the poor and the hungry issues that we've been talking about,

733
00:40:29,960 --> 00:40:33,360
Speaker 1: or something that doesn't require people to necessarily have full

734
00:40:33,520 --> 00:40:36,880
Speaker 1: access to the set of complications that are involved in

735
00:40:36,920 --> 00:40:39,200
Speaker 1: scientific research. Essentially, I think what they mean by that

736
00:40:39,320 --> 00:40:42,920
Speaker 1: is like, you don't have to have a PhD. Right, Um,

737
00:40:43,040 --> 00:40:46,960
Speaker 1: So we already have really scared science resources, as we

738
00:40:47,040 --> 00:40:49,360
Speaker 1: know from all the stuff that we cover for the

739
00:40:49,440 --> 00:40:51,439
Speaker 1: show and for the other stuff that we work on here.

740
00:40:51,719 --> 00:40:54,480
Speaker 1: How do we focus our solutions for the right kind

741
00:40:54,520 --> 00:40:58,080
Speaker 1: of stakeholders? While in Maine, she says, researchers are trying

742
00:40:58,080 --> 00:41:01,680
Speaker 1: to tackle this problem differently and specifically so that the

743
00:41:01,680 --> 00:41:05,719
Speaker 1: way that they address sustainability. So they're bringing together shellfish

744
00:41:05,800 --> 00:41:10,360
Speaker 1: harvesters with their policy makers and biologists and economists to

745
00:41:10,400 --> 00:41:13,719
Speaker 1: all discuss the issues surrounding What I would assume is

746
00:41:13,800 --> 00:41:19,960
Speaker 1: you know of farming shellfish for for food industry. Another

747
00:41:20,000 --> 00:41:24,400
Speaker 1: example she gave was solving the decline in mains blueberry crops,

748
00:41:24,680 --> 00:41:27,360
Speaker 1: which they see as also being connected to the collapse

749
00:41:27,680 --> 00:41:31,040
Speaker 1: of their pollinator be population. Now, I don't know how

750
00:41:31,080 --> 00:41:34,040
Speaker 1: many times in different forms are shows here at how

751
00:41:34,080 --> 00:41:36,719
Speaker 1: stuff works. We've talked about colony collapse disorder. It's something

752
00:41:36,760 --> 00:41:39,480
Speaker 1: that's on a lot of people's radar, but this is

753
00:41:39,520 --> 00:41:42,400
Speaker 1: an interesting way to approach it, that it's not just Okay,

754
00:41:42,440 --> 00:41:45,080
Speaker 1: that's a science issue we need to solve, but also, hey,

755
00:41:45,080 --> 00:41:47,279
Speaker 1: we've got these blueberries over here that are important to

756
00:41:47,280 --> 00:41:51,160
Speaker 1: our economy. What does that mean for this you know? Uh?

757
00:41:51,200 --> 00:41:55,279
Speaker 1: And then ultimately her she says, there's also scientists who

758
00:41:55,280 --> 00:41:58,000
Speaker 1: are going to argue against this, so be prepared for

759
00:41:58,040 --> 00:42:01,160
Speaker 1: a backlash. And there's lots of scientists who claim that

760
00:42:01,239 --> 00:42:04,600
Speaker 1: anyone who doesn't have formal training, they won't do any

761
00:42:04,600 --> 00:42:07,520
Speaker 1: good research, right, They're not going to be capable of

762
00:42:07,800 --> 00:42:11,319
Speaker 1: contributing to the discipline. It's what in academia is often

763
00:42:11,360 --> 00:42:15,000
Speaker 1: referred to as the siloing effect, right where everybody puts

764
00:42:15,040 --> 00:42:17,640
Speaker 1: themselves in little silos and they sort of have their

765
00:42:17,719 --> 00:42:20,120
Speaker 1: their beefdoms that they want to fight over control full

766
00:42:20,160 --> 00:42:22,880
Speaker 1: of her well, in Nane, this is exactly how the

767
00:42:22,880 --> 00:42:25,680
Speaker 1: the Arrowhead facility opened up, that rift into the Todash

768
00:42:25,760 --> 00:42:28,920
Speaker 1: darkness exactly. It's you know what, like the Mists would

769
00:42:28,920 --> 00:42:31,440
Speaker 1: not have happened if we just approached things as a

770
00:42:31,440 --> 00:42:34,440
Speaker 1: wicked problem. I know, all those people in that grocery store. Man. Yeah,

771
00:42:34,560 --> 00:42:36,480
Speaker 1: it was a tough time. You know what, That's a

772
00:42:36,480 --> 00:42:39,120
Speaker 1: good uh segue for us to bring it down where

773
00:42:39,239 --> 00:42:41,200
Speaker 1: we've been up. We've been up in the outer space

774
00:42:41,200 --> 00:42:44,239
Speaker 1: of todash darkness, talking about the macro version of this,

775
00:42:44,360 --> 00:42:46,480
Speaker 1: talking about the science version of it. Let's bring it

776
00:42:46,480 --> 00:42:50,840
Speaker 1: down to the micro level, right. Uh. We've all worked

777
00:42:50,880 --> 00:42:54,560
Speaker 1: for organizations, presumably right in our current society. That's how

778
00:42:54,680 --> 00:42:57,399
Speaker 1: we can afford the devices that we have to listen

779
00:42:57,440 --> 00:43:00,400
Speaker 1: to podcasts on. Yeah, even if you don't have to

780
00:43:00,760 --> 00:43:03,920
Speaker 1: work for an organization, you're probably having to come in

781
00:43:03,960 --> 00:43:06,879
Speaker 1: contact with organizations. You know. If that alone, hit man,

782
00:43:06,960 --> 00:43:09,400
Speaker 1: you still have to work for the mafia. Yeah, it's true.

783
00:43:09,719 --> 00:43:12,600
Speaker 1: It's true. So what we found when we were looking

784
00:43:12,600 --> 00:43:15,680
Speaker 1: at the wicked problem thing is, yeah, it's being applied

785
00:43:15,920 --> 00:43:19,600
Speaker 1: in the science aspect up in Maine. But by and large,

786
00:43:19,760 --> 00:43:22,160
Speaker 1: almost all the research that was showing up for me

787
00:43:22,719 --> 00:43:26,239
Speaker 1: was management stuff or business review type magazines. And the

788
00:43:26,280 --> 00:43:30,040
Speaker 1: big one that I looked to was a piece by

789
00:43:30,160 --> 00:43:33,239
Speaker 1: John Camillis that was in the Harvard Business Review in

790
00:43:33,239 --> 00:43:37,760
Speaker 1: two thousand and eight. Uh And basically, uh, he studied

791
00:43:37,800 --> 00:43:41,160
Speaker 1: strategic planning at twenty two different companies. Then he looked

792
00:43:41,200 --> 00:43:44,560
Speaker 1: in depth at seven of them, and he finally zoned

793
00:43:44,600 --> 00:43:47,600
Speaker 1: in specifically on DuPont Pharmaceuticals, which has come up on

794
00:43:47,640 --> 00:43:49,960
Speaker 1: the show before. I don't I can't remember at the

795
00:43:50,000 --> 00:43:52,600
Speaker 1: top of my head was Alexander Shulgan who worked for DuPont.

796
00:43:53,239 --> 00:43:55,960
Speaker 1: It was somebody like It wasn't, Yeah, I think it

797
00:43:56,680 --> 00:44:00,480
Speaker 1: might have been shun Um. It wasn't really no, right,

798
00:44:00,760 --> 00:44:02,799
Speaker 1: So he finally he zoned in on DuPont to kind

799
00:44:02,800 --> 00:44:06,040
Speaker 1: of see how that company drew up its strategies to

800
00:44:06,160 --> 00:44:09,279
Speaker 1: deal with uncertainty, and he used all of these to

801
00:44:09,360 --> 00:44:13,919
Speaker 1: come up with ways to talk about taming problems within

802
00:44:13,960 --> 00:44:19,439
Speaker 1: the workplace, the ones that can't be solved, the wicked problems. So, yeah,

803
00:44:19,480 --> 00:44:23,319
Speaker 1: camillisit basically makes the same argument that those guys made

804
00:44:23,320 --> 00:44:26,719
Speaker 1: back in the early seventies, but in terms of companies, right,

805
00:44:26,760 --> 00:44:29,759
Speaker 1: So he says, companies are faced with constant, wicked problems

806
00:44:29,760 --> 00:44:33,840
Speaker 1: in their increasingly complex and violent environment. So you're looking

807
00:44:33,880 --> 00:44:38,160
Speaker 1: at changing the way that we look at strategic planning processes,

808
00:44:38,160 --> 00:44:41,920
Speaker 1: which are very traditional. They don't address wicked problems in

809
00:44:41,960 --> 00:44:46,160
Speaker 1: any way. So in fact, the actual processes that are

810
00:44:46,360 --> 00:44:49,480
Speaker 1: used to approach the problems may and sell it may

811
00:44:49,520 --> 00:44:53,680
Speaker 1: in fact be wicked problems themselves. Right every time you yeah,

812
00:44:53,719 --> 00:44:57,120
Speaker 1: every time you change the structure of the business, potentially

813
00:44:57,160 --> 00:44:59,560
Speaker 1: new wicked problem. Right. Yeah. I'm thinking of like every

814
00:44:59,600 --> 00:45:03,239
Speaker 1: time like a company goes through a reorganization, right, or

815
00:45:03,239 --> 00:45:06,880
Speaker 1: a restructuring pivots, Yes, the pivoting, which is something we

816
00:45:06,920 --> 00:45:11,960
Speaker 1: hear about quite often. Um yeah, or uh you know,

817
00:45:12,400 --> 00:45:15,160
Speaker 1: big surprise. This piece was written eight years ago, and

818
00:45:15,200 --> 00:45:18,400
Speaker 1: it doesn't seem like policymakers and companies have really acknowledged

819
00:45:18,440 --> 00:45:22,440
Speaker 1: it yet. You know, I've I've worked here for three years.

820
00:45:22,440 --> 00:45:26,279
Speaker 1: I've worked in the public sector, in academia and in libraries.

821
00:45:26,320 --> 00:45:28,359
Speaker 1: I used to work for a publishing company before this,

822
00:45:28,440 --> 00:45:31,880
Speaker 1: and then long ago, I was a graphic designer working

823
00:45:31,960 --> 00:45:36,040
Speaker 1: in a sort of direct marketing world, and I didn't

824
00:45:36,040 --> 00:45:39,080
Speaker 1: see it. Any of those businesses a sort of recognition,

825
00:45:39,200 --> 00:45:44,399
Speaker 1: recognition of the like organizational principle of the wicked problem. Um.

826
00:45:44,440 --> 00:45:46,560
Speaker 1: And I don't know, I don't know. Maybe they're out there.

827
00:45:46,600 --> 00:45:48,160
Speaker 1: I'd love to hear it. If some of you out

828
00:45:48,160 --> 00:45:52,080
Speaker 1: there listening work for a company that thinks about running

829
00:45:52,120 --> 00:45:54,239
Speaker 1: the company in such a way let us know, because

830
00:45:54,280 --> 00:45:56,399
Speaker 1: it sounds like it would be, I don't know, sort

831
00:45:56,400 --> 00:45:59,080
Speaker 1: of heavenly place to work at. Um. Well even know,

832
00:45:59,120 --> 00:46:01,440
Speaker 1: one of the issues is that I feel like a

833
00:46:01,520 --> 00:46:05,200
Speaker 1: lot of workplaces and I'm and i'm speaking you know,

834
00:46:06,160 --> 00:46:08,120
Speaker 1: I'm speaking to it to My history with with various

835
00:46:08,120 --> 00:46:11,960
Speaker 1: employers over the years is that, um it's one thing

836
00:46:12,000 --> 00:46:15,120
Speaker 1: to to have a meeting about a problem, to try

837
00:46:15,120 --> 00:46:18,000
Speaker 1: and to find that problem in a way that's that's good. Like,

838
00:46:18,080 --> 00:46:20,960
Speaker 1: that's how you should approach wicked problems, is to not

839
00:46:21,360 --> 00:46:24,040
Speaker 1: just throw out solutions willy nilly and see what happens.

840
00:46:24,160 --> 00:46:26,000
Speaker 1: You should discuss it. You should try and get figure

841
00:46:26,000 --> 00:46:27,759
Speaker 1: out what are some of the route issues here, what

842
00:46:27,800 --> 00:46:30,840
Speaker 1: are the different viewpoints. But a lot of times in

843
00:46:30,840 --> 00:46:33,719
Speaker 1: a corporate environment, like that's all that gets done. Like

844
00:46:34,200 --> 00:46:37,239
Speaker 1: you have those meetings, the results are are are typed up,

845
00:46:37,440 --> 00:46:41,960
Speaker 1: and the wicked problem UM summary winds up on somebody's desk.

846
00:46:42,320 --> 00:46:45,000
Speaker 1: Maybe there are some recommended solutions, Maybe some of those

847
00:46:45,040 --> 00:46:48,480
Speaker 1: get implemented, maybe the safer ones get implemented. But does

848
00:46:48,800 --> 00:46:53,120
Speaker 1: does anything ultimately change? Maybe not. Yeah, that's actually interesting

849
00:46:53,120 --> 00:46:57,960
Speaker 1: because Camillus, you know, one of his recommendations is essentially uh,

850
00:46:58,239 --> 00:46:59,480
Speaker 1: right at the top, he says, well, you got to

851
00:46:59,520 --> 00:47:03,000
Speaker 1: involve your stakeholders and document everybody's opinions and then make

852
00:47:03,040 --> 00:47:05,960
Speaker 1: sure that everybody's communicating about what those opinions are. And

853
00:47:05,960 --> 00:47:07,840
Speaker 1: that's what you just described. But you're right, in a

854
00:47:07,920 --> 00:47:11,000
Speaker 1: lot of situations just kind of stops there, right, Um,

855
00:47:11,040 --> 00:47:15,600
Speaker 1: Like there, there isn't the action part that comes after it. Um.

856
00:47:15,640 --> 00:47:18,239
Speaker 1: And he he actually came up with his own five

857
00:47:18,400 --> 00:47:21,359
Speaker 1: system symptoms based off of all these other people came

858
00:47:21,400 --> 00:47:23,880
Speaker 1: up with and they're fairly similar. Um, you know, we

859
00:47:23,920 --> 00:47:26,440
Speaker 1: get the same thing with there's many stakeholders. They all

860
00:47:26,440 --> 00:47:29,000
Speaker 1: have different values and different priorities. Of course, you see

861
00:47:29,000 --> 00:47:31,680
Speaker 1: that in the workplace, right like some managers of some

862
00:47:31,760 --> 00:47:34,760
Speaker 1: departments have their own values and priorities while another manager

863
00:47:34,800 --> 00:47:37,879
Speaker 1: and another tier above them into the side of them,

864
00:47:37,880 --> 00:47:40,680
Speaker 1: has a different set of priorities. It is not necessarily

865
00:47:40,719 --> 00:47:43,560
Speaker 1: being communicated clearly or in the worst situations, you're you're

866
00:47:43,560 --> 00:47:45,680
Speaker 1: one of those employees who find yourself with two or

867
00:47:45,680 --> 00:47:48,759
Speaker 1: more bosses, figure out who am I listening to what

868
00:47:48,920 --> 00:47:51,840
Speaker 1: and what is the priority? And just like the larger

869
00:47:51,880 --> 00:47:57,040
Speaker 1: macro problems, you know, they've got complex, tangled roots their problem.

870
00:47:57,760 --> 00:48:00,239
Speaker 1: It's difficult to come to grips with and every time

871
00:48:00,280 --> 00:48:03,440
Speaker 1: you try to approach it it changes. I'm thinking like

872
00:48:03,440 --> 00:48:05,640
Speaker 1: like I'm thinking of previous workplaces I've been at, where

873
00:48:05,680 --> 00:48:08,600
Speaker 1: like there's been a problem employee, right, and it's just

874
00:48:08,640 --> 00:48:11,920
Speaker 1: like a person that everybody knows is a problem, and

875
00:48:12,400 --> 00:48:15,000
Speaker 1: you go the simple solution is just fire that person.

876
00:48:15,239 --> 00:48:18,080
Speaker 1: But like in certain atmospheres, you can't just do that, right,

877
00:48:18,120 --> 00:48:21,480
Speaker 1: because there's repercussions to that as well that subsequently tangle

878
00:48:21,600 --> 00:48:26,200
Speaker 1: and lead to other problems too. I have unfortunately seen

879
00:48:26,239 --> 00:48:30,200
Speaker 1: that many many workplaces. Um again, they have no precedent,

880
00:48:30,560 --> 00:48:33,480
Speaker 1: there's nothing to indicate what the right answer is, right,

881
00:48:33,520 --> 00:48:36,120
Speaker 1: there's no Like I love how we we all think

882
00:48:36,160 --> 00:48:38,919
Speaker 1: of HR human resources is being like, oh, well they've

883
00:48:38,960 --> 00:48:41,319
Speaker 1: got the answer, right, Like there's a handbook they go

884
00:48:41,360 --> 00:48:43,239
Speaker 1: to school for that. So clearly there's there's gotta be

885
00:48:43,280 --> 00:48:45,080
Speaker 1: a way to approach this in a particular way that

886
00:48:45,120 --> 00:48:49,520
Speaker 1: has the answer. But I don't necessarily know that that's true. Yeah,

887
00:48:49,560 --> 00:48:50,880
Speaker 1: I mean you get to say that they have a

888
00:48:51,000 --> 00:48:53,200
Speaker 1: they have a system, they may have a guide, they

889
00:48:53,200 --> 00:48:56,160
Speaker 1: may have a way to discuss a problem that arises,

890
00:48:56,280 --> 00:48:59,360
Speaker 1: but ultimately the the I mean, we've all heard of

891
00:48:59,400 --> 00:49:03,520
Speaker 1: situations with people we know where the the HR solution

892
00:49:03,680 --> 00:49:07,960
Speaker 1: is not the best solution. Yeah, certainly, certainly. Yeah. Um.

893
00:49:08,280 --> 00:49:10,600
Speaker 1: And it's interesting too because, like my wife has worked

894
00:49:10,600 --> 00:49:12,640
Speaker 1: at all different kinds of companies as well too. I

895
00:49:12,640 --> 00:49:14,880
Speaker 1: feel like between the two of us over the last

896
00:49:14,920 --> 00:49:18,400
Speaker 1: fifteen years, we've had like this very broad spectrum of

897
00:49:18,480 --> 00:49:20,680
Speaker 1: types of places to work at, and yet like we

898
00:49:20,719 --> 00:49:23,080
Speaker 1: see the same problems that all of them, you know, Yeah,

899
00:49:23,120 --> 00:49:24,680
Speaker 1: I mean, in a way, it comes down to what

900
00:49:24,760 --> 00:49:28,719
Speaker 1: was the famous Reagan quote about government, about government isn't

901
00:49:28,760 --> 00:49:31,160
Speaker 1: the solution of the problems? Government is the problem? Which

902
00:49:31,600 --> 00:49:33,319
Speaker 1: is you know, I think going a bit too far,

903
00:49:33,400 --> 00:49:37,279
Speaker 1: but it does tie in to the basic idea that

904
00:49:37,600 --> 00:49:40,560
Speaker 1: the body that tries to fix a wicked problem, be

905
00:49:40,719 --> 00:49:44,360
Speaker 1: it you know, a large sweeping macro problem, more micro problem,

906
00:49:44,400 --> 00:49:47,360
Speaker 1: the body that tries to fix something almost inevitably messes

907
00:49:47,400 --> 00:49:50,200
Speaker 1: it up or messes it up for some people. Yeah,

908
00:49:50,239 --> 00:49:53,040
Speaker 1: it's like it's almost like you have to figure out, like, Okay,

909
00:49:53,480 --> 00:49:55,719
Speaker 1: they're gonna mess this up, But is the mess that's

910
00:49:55,719 --> 00:49:57,719
Speaker 1: going to be left afterwards better than the mess that

911
00:49:57,760 --> 00:50:00,279
Speaker 1: we have, Yeah, what is my relationship to the mass?

912
00:50:00,520 --> 00:50:02,759
Speaker 1: Want to be like I know, like you know, I

913
00:50:02,800 --> 00:50:05,400
Speaker 1: know it's gonna be messy, but can I live with

914
00:50:05,440 --> 00:50:10,080
Speaker 1: the mass. So let's quickly go through Camillis's solutions, which

915
00:50:10,080 --> 00:50:12,279
Speaker 1: are beyond just like what you described, which is the

916
00:50:12,280 --> 00:50:15,080
Speaker 1: sort of Yeah, everybody sit around and talk about a thing.

917
00:50:15,120 --> 00:50:16,560
Speaker 1: We'll write it down and we'll put it in a

918
00:50:16,600 --> 00:50:20,399
Speaker 1: document somewhere and file it away. Um, does this sound

919
00:50:20,480 --> 00:50:25,080
Speaker 1: familiar for anybody out there? Define what your corporate identity is? Right?

920
00:50:25,239 --> 00:50:29,120
Speaker 1: What what are the company's values? What is it competent at?

921
00:50:29,200 --> 00:50:31,799
Speaker 1: And what are its aspirations. I can't tell you how

922
00:50:31,800 --> 00:50:35,760
Speaker 1: many places I've worked for that, uh that those aren't

923
00:50:35,760 --> 00:50:38,360
Speaker 1: clear to all the employees. And yet like it seems

924
00:50:38,400 --> 00:50:41,800
Speaker 1: like something that should just be relatively simple, right, even

925
00:50:41,840 --> 00:50:44,279
Speaker 1: like when we see like fictional versions of companies and

926
00:50:44,360 --> 00:50:52,240
Speaker 1: thinking like I'm thinking of what's the evil company in RoboCop? Uh? Yeah, yeah,

927
00:50:52,480 --> 00:50:54,919
Speaker 1: Like that's a perfect example. Like the people who worked

928
00:50:54,960 --> 00:50:57,319
Speaker 1: there seemed to pretty much have an idea of what

929
00:50:57,400 --> 00:51:00,560
Speaker 1: its values were, what they were good at building killer robots,

930
00:51:00,960 --> 00:51:03,960
Speaker 1: and what its aspirations were, which is basically taken over

931
00:51:03,960 --> 00:51:06,000
Speaker 1: the city of Detroit. Right, But you find in a

932
00:51:06,040 --> 00:51:09,200
Speaker 1: lot of situations that there's there's like a vague sort

933
00:51:09,200 --> 00:51:14,320
Speaker 1: of anxiety inducing uh, amorphous nous to what the company

934
00:51:14,360 --> 00:51:16,840
Speaker 1: you work for is doing. Right, Yeah. I mean meanwhile,

935
00:51:16,960 --> 00:51:19,600
Speaker 1: you go to like a your average kindergarten class and

936
00:51:20,120 --> 00:51:23,920
Speaker 1: generally the rules are on the wall, right exactly. That

937
00:51:23,920 --> 00:51:25,600
Speaker 1: would be great if we had that like on the

938
00:51:25,640 --> 00:51:29,200
Speaker 1: refrigerator or something at all different corporations, like here's what

939
00:51:29,280 --> 00:51:34,760
Speaker 1: it is, it's written in kran There's also, of course,

940
00:51:34,800 --> 00:51:37,439
Speaker 1: like what Robert was mentioning earlier, you got to take

941
00:51:37,480 --> 00:51:39,399
Speaker 1: action on things. And this connects to what we were

942
00:51:39,440 --> 00:51:42,640
Speaker 1: talking about with the rapid prototyping. So instead of thinking

943
00:51:42,680 --> 00:51:46,680
Speaker 1: through every option that's available before choosing a single one,

944
00:51:47,120 --> 00:51:51,240
Speaker 1: they recommend what Camillis does in particular, experimenting with multiple

945
00:51:51,320 --> 00:51:55,200
Speaker 1: strategies that seem like they're feasible, uh, and launch innovative

946
00:51:55,200 --> 00:51:59,440
Speaker 1: pilot programs. And this is interesting to me because this

947
00:51:59,520 --> 00:52:01,640
Speaker 1: is something actually at how stuff works. We've heard a

948
00:52:01,680 --> 00:52:03,879
Speaker 1: lot in the last i'd say two years maybe, which

949
00:52:03,920 --> 00:52:07,600
Speaker 1: is don't be afraid to fail, right, And there's a

950
00:52:07,600 --> 00:52:09,759
Speaker 1: At first I had trouble struggling with that and now

951
00:52:09,760 --> 00:52:11,799
Speaker 1: I sort of see, oh okay, so this is that

952
00:52:11,840 --> 00:52:14,640
Speaker 1: approach that I don't know that rapid prototyping is the

953
00:52:14,719 --> 00:52:18,120
Speaker 1: right term. The way I've often heard it described as

954
00:52:18,160 --> 00:52:22,240
Speaker 1: the whole fail, fail quickly, and fail often, you know, yeah,

955
00:52:22,600 --> 00:52:25,120
Speaker 1: which is try a bunch of different things, which is

956
00:52:25,160 --> 00:52:27,600
Speaker 1: you know sort of that's okay, then we know not

957
00:52:27,680 --> 00:52:30,120
Speaker 1: to do that one approach. Yeah, Yeah, Like generally a

958
00:52:30,200 --> 00:52:32,399
Speaker 1: b testing is is uh is that it's a great

959
00:52:32,400 --> 00:52:33,880
Speaker 1: way to do this. You just roll it out for

960
00:52:33,920 --> 00:52:35,279
Speaker 1: some people and you show them a you show them

961
00:52:35,320 --> 00:52:37,200
Speaker 1: be figure out what works, and you go with that

962
00:52:37,239 --> 00:52:39,720
Speaker 1: and you do this and you know, with without having

963
00:52:39,760 --> 00:52:45,000
Speaker 1: to invest much more time and money in testing the product,

964
00:52:45,080 --> 00:52:47,120
Speaker 1: and you bring it back to the macro level for

965
00:52:47,160 --> 00:52:49,719
Speaker 1: a second here, and you go, oh wow, Like, there's

966
00:52:49,760 --> 00:52:52,880
Speaker 1: no way that governments can act this way, right, because

967
00:52:52,880 --> 00:52:55,360
Speaker 1: if they're just like, well, we'll just try twenty different

968
00:52:55,400 --> 00:52:57,440
Speaker 1: things and if nineteen of them fail, at least we'll

969
00:52:57,440 --> 00:53:00,600
Speaker 1: have found one thing that works. There's plenty of people

970
00:53:00,600 --> 00:53:02,800
Speaker 1: out there who go, what about all my tax dollars

971
00:53:02,800 --> 00:53:05,239
Speaker 1: that were just spent on the nineteen things that didn't work? Right?

972
00:53:05,680 --> 00:53:10,439
Speaker 1: So there's an inherently a wicked problem there as well well,

973
00:53:10,719 --> 00:53:12,680
Speaker 1: and a lot of them a lot of a lot

974
00:53:12,680 --> 00:53:14,680
Speaker 1: of businesses or maybe maybe have a lot more in

975
00:53:14,719 --> 00:53:20,400
Speaker 1: common with dictatorships as opposed to a democratic republic. So

976
00:53:21,440 --> 00:53:22,919
Speaker 1: you know, it's a little more a little a little

977
00:53:22,960 --> 00:53:25,880
Speaker 1: more complicated on on the macro level life well, spinning

978
00:53:25,920 --> 00:53:30,080
Speaker 1: off your dictatorship metaphor. It's kind of interesting because dictators

979
00:53:30,320 --> 00:53:35,440
Speaker 1: don't necessarily have this one particular communication orientation that Camillis recommends,

980
00:53:35,520 --> 00:53:39,080
Speaker 1: and I like this. Uh, it really stems out of

981
00:53:39,120 --> 00:53:43,560
Speaker 1: the basic necessity of all kinds of human communication, and

982
00:53:43,600 --> 00:53:47,240
Speaker 1: it is to adopt what's called a feed forward orientation.

983
00:53:47,640 --> 00:53:51,160
Speaker 1: So when you're trying to solve problems, don't just use

984
00:53:51,400 --> 00:53:57,520
Speaker 1: feedback for communicating with your organization about what the problems

985
00:53:57,520 --> 00:54:00,959
Speaker 1: are and how to tame them, because feed back rely

986
00:54:01,239 --> 00:54:05,360
Speaker 1: solely on the past and what happened, while wicked problems

987
00:54:05,400 --> 00:54:08,560
Speaker 1: all arise out of an unclear future, right, So remember

988
00:54:08,560 --> 00:54:12,680
Speaker 1: that that indeterminable scope that they have, um, so you

989
00:54:12,719 --> 00:54:17,200
Speaker 1: really need to envision that that future that's that's unclear

990
00:54:17,239 --> 00:54:19,560
Speaker 1: and try to envision what you'd like it to be.

991
00:54:19,640 --> 00:54:21,960
Speaker 1: So this gets back to the aspirations of what of

992
00:54:21,960 --> 00:54:26,399
Speaker 1: who you're working for, and then communicate what that organization

993
00:54:26,640 --> 00:54:29,799
Speaker 1: wants its future to look like to everybody involved in

994
00:54:29,840 --> 00:54:32,160
Speaker 1: the organization. You know, it's interesting because some of these

995
00:54:32,200 --> 00:54:36,640
Speaker 1: ideas regarding the corporate environment, they have spilled over into

996
00:54:36,680 --> 00:54:41,440
Speaker 1: sort of family management uh scenarios there. For instance, in

997
00:54:41,760 --> 00:54:44,960
Speaker 1: my family, UM, me and my wife and my son,

998
00:54:45,360 --> 00:54:48,279
Speaker 1: we try and have a weekly meeting, and at the

999
00:54:48,320 --> 00:54:53,280
Speaker 1: weekly meeting, everybody has to has to discuss what worked

1000
00:54:53,360 --> 00:54:55,520
Speaker 1: during the week, what didn't work during the week, what

1001
00:54:55,600 --> 00:54:58,200
Speaker 1: they would like to change in the coming week, as

1002
00:54:58,200 --> 00:54:59,799
Speaker 1: well as like what we would like to eat in

1003
00:54:59,800 --> 00:55:02,440
Speaker 1: the coming and things like that, uh and that. But

1004
00:55:02,520 --> 00:55:04,600
Speaker 1: that approach is based on some of the principles that

1005
00:55:04,640 --> 00:55:08,080
Speaker 1: have been bouncing around in the corporate world for the

1006
00:55:08,080 --> 00:55:11,080
Speaker 1: past ten years or so. Yeah, that is an interesting approach.

1007
00:55:11,120 --> 00:55:14,799
Speaker 1: And essentially what we're talking about here is just open communication,

1008
00:55:14,960 --> 00:55:18,000
Speaker 1: which surprisingly, you know, for human beings, which like one

1009
00:55:18,000 --> 00:55:20,680
Speaker 1: of our greatest assets is our ability to communicate with

1010
00:55:20,719 --> 00:55:23,279
Speaker 1: one another. We're not so good at at at doing

1011
00:55:23,320 --> 00:55:26,000
Speaker 1: it in these kind of situations where we're tackling these

1012
00:55:26,120 --> 00:55:28,160
Speaker 1: real world, big problems. Yeah, I mean, we did a

1013
00:55:28,160 --> 00:55:30,600
Speaker 1: workbook uh for it where we had to. We even

1014
00:55:30,640 --> 00:55:32,880
Speaker 1: had to come up with our own essentially our corporate

1015
00:55:32,920 --> 00:55:35,839
Speaker 1: identity for like, what our what's our motto? What are

1016
00:55:35,880 --> 00:55:39,960
Speaker 1: our values? I bet Bastion had a giraffe in there somewhere. Um,

1017
00:55:40,040 --> 00:55:42,240
Speaker 1: you know, he was not he was not super helpful

1018
00:55:42,280 --> 00:55:46,920
Speaker 1: in the crafting the of of this particular document. But

1019
00:55:47,000 --> 00:55:49,759
Speaker 1: that sounds like good advice for for any you know,

1020
00:55:50,160 --> 00:55:53,319
Speaker 1: uh family unit. You know, so you know, who are

1021
00:55:53,360 --> 00:55:55,719
Speaker 1: we what what are we trying to do here? What's

1022
00:55:55,719 --> 00:55:58,279
Speaker 1: this little family? You know, let's get out of the

1023
00:55:59,120 --> 00:56:01,120
Speaker 1: moment to moment ing and just think a little a

1024
00:56:01,160 --> 00:56:03,840
Speaker 1: little broader. Yeah, I like that. That's cool. Well, it

1025
00:56:03,880 --> 00:56:05,719
Speaker 1: sounds like that you can take that and you can

1026
00:56:05,760 --> 00:56:08,240
Speaker 1: extrapolate it out words and apply it on the work level.

1027
00:56:08,440 --> 00:56:10,640
Speaker 1: You're can apply it on the science level, and then

1028
00:56:10,640 --> 00:56:12,600
Speaker 1: you can apply it on the sort of macro scale

1029
00:56:12,680 --> 00:56:15,720
Speaker 1: level that we've been talking about. So that really gets

1030
00:56:15,719 --> 00:56:18,360
Speaker 1: at the gist of wicked problems we weren't able to.

1031
00:56:18,480 --> 00:56:21,880
Speaker 1: I mean, you know, obviously it's much denser than what

1032
00:56:21,920 --> 00:56:23,840
Speaker 1: we talked about today, and I feel like this is

1033
00:56:23,880 --> 00:56:26,319
Speaker 1: maybe a little denser than most stuff to blow your

1034
00:56:26,360 --> 00:56:29,120
Speaker 1: mind episodes are. But you know, we we at least

1035
00:56:29,120 --> 00:56:31,680
Speaker 1: covered the surface of this is what they are, this

1036
00:56:31,760 --> 00:56:33,360
Speaker 1: is how they apply to the real world that we

1037
00:56:33,400 --> 00:56:36,200
Speaker 1: exist in and science. Yeah, and I have no doubt

1038
00:56:36,280 --> 00:56:39,359
Speaker 1: that we will refer back to wicked problems in the

1039
00:56:39,400 --> 00:56:43,520
Speaker 1: future as we tackle other other topics, be they you know,

1040
00:56:43,760 --> 00:56:47,920
Speaker 1: ultimately scientific or or more likely cultural. So, those of

1041
00:56:47,960 --> 00:56:51,680
Speaker 1: you out there listening, uh, let us know. I'm really curious.

1042
00:56:52,080 --> 00:56:54,560
Speaker 1: I'm always curious to see what our audience has to

1043
00:56:54,600 --> 00:56:57,600
Speaker 1: say about our episodes, but in this instance, in particular,

1044
00:56:57,760 --> 00:56:59,560
Speaker 1: I'd like to see what you think about the theory

1045
00:56:59,640 --> 00:57:02,520
Speaker 1: of wicked problems, how it's applicable in your life, or

1046
00:57:02,560 --> 00:57:04,600
Speaker 1: how you could see it being applicable maybe on a

1047
00:57:04,680 --> 00:57:07,400
Speaker 1: larger scale. Or those of you out there, we know

1048
00:57:07,440 --> 00:57:08,799
Speaker 1: a lot of people who listen to the show are

1049
00:57:08,840 --> 00:57:12,480
Speaker 1: graduate students or actual scientists working in laboratories. How it

1050
00:57:12,480 --> 00:57:15,960
Speaker 1: affects the work that you're doing. Yeah, indeed, and how

1051
00:57:16,000 --> 00:57:19,360
Speaker 1: do you personally tackle wicked problems? I don't think about

1052
00:57:19,400 --> 00:57:22,440
Speaker 1: them because they're they're wicked problems that that exist. Uh,

1053
00:57:22,520 --> 00:57:24,360
Speaker 1: you know within a country. There we could problems that

1054
00:57:24,400 --> 00:57:27,840
Speaker 1: exist within a family. Uh, how do you dance around

1055
00:57:27,880 --> 00:57:30,840
Speaker 1: those and define them? So usual places to reach out

1056
00:57:30,880 --> 00:57:32,800
Speaker 1: to us and let us know your thoughts on these things.

1057
00:57:33,040 --> 00:57:36,200
Speaker 1: We've got Facebook, we're on Twitter, we are on Tumbler,

1058
00:57:36,840 --> 00:57:39,840
Speaker 1: we're even on Instagram. Now we're gonna start posting to

1059
00:57:39,960 --> 00:57:42,920
Speaker 1: that soon and you can start seeing pictures of things

1060
00:57:43,000 --> 00:57:46,280
Speaker 1: that we're taking and of us, and probably images I

1061
00:57:46,280 --> 00:57:49,720
Speaker 1: would assume of the podcast episodes that were distributing as well. Yeah,

1062
00:57:49,760 --> 00:57:51,919
Speaker 1: we'll blow the mind on there. I think currently there's

1063
00:57:51,960 --> 00:57:54,920
Speaker 1: just one picture of me with a third ey Oh okay,

1064
00:57:54,960 --> 00:57:57,520
Speaker 1: well that's a good place to start. Uh. And then,

1065
00:57:57,600 --> 00:57:59,720
Speaker 1: of course, how else could they reach out to us

1066
00:57:59,720 --> 00:58:01,920
Speaker 1: to just discussed their wicked problems. Oh, just get in

1067
00:58:01,920 --> 00:58:04,200
Speaker 1: touch with us the old facting way. Email us at

1068
00:58:04,280 --> 00:58:15,800
Speaker 1: below the mind at how stuff works dot com for

1069
00:58:15,960 --> 00:58:18,240
Speaker 1: more on this and thousands of other topics. Is it

1070
00:58:18,360 --> 00:58:42,320
Speaker 1: how stuff works dot com